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Meng, Ning Ning, Guang Xue Zhang, Gao Qun Wei, and Xin Lv. "Study on Three-Dimensional Geological Modeling of Reservoir in Hei46 Block." Advanced Materials Research 1073-1076 (December 2014): 2349–52. http://dx.doi.org/10.4028/www.scientific.net/amr.1073-1076.2349.
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Three dimensional (3D) geologic modeling is used to study the reservoir quantitatively from a three-dimensional angle, and its core is the prediction to reservoir of multi-disciplinary integration, quantitative and visualization. Compared with traditional reservoir research, it has a significant advantage. This paper makes geological modeling research and builds structural models sedimentary micro-facies models and phased property model for Hei46 block of Daqingzi oilfield by utilizing 3D geologic modeling technique and petrel software on the basis of integrated using of geology, logging, oil production test, production of dynamic information, thus it provide a reliable basis for reservoir's development and adjustment.
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Yu, Jiang Tao, Jin Liang Zhang, and Shuang Yan Chen. "Application of Three-Dimensional Fine Geological Modeling in Complex Fault-Block Reservoir with Low Permeability." Applied Mechanics and Materials 511-512 (February 2014): 779–82. http://dx.doi.org/10.4028/www.scientific.net/amm.511-512.779.
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Three dimensional geologic modeling is a powerful tool for reservoir development stages of geological study, it can solves many traditional problems existing in geological research through the establishment of precise three dimensional geologic modeling and represents an important direction for the further development of oilfield geological research. Low permeability and thin interbed reservoir of complex fault block have the characteristics of severe heterogeneity, complex relations of oil-water distribution, poor development effect, it is necessary to built high precision three dimensional geologic modeling in the process of oilfield exploration and to fine reservoir description and prediction on this basis, finally reach the purpose of reduce the risk of development and improve the economic benefit. This paper makes geological modeling research and builds structural models sedimentary micro-facies models and phased property model for Zhuzhuang block of Jiangsu oilfield by utilizing three dimensional geologic modeling technique and petrel geology modeling software on the basis of integrated using of geology, logging, oil production test, production of dynamic information, thus it provide a solid basis for reservoir's development and adjustment.
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Mayall, Michael. "Advances in reservoir geology." Marine and Petroleum Geology 11, no. 3 (June 1994): 412. http://dx.doi.org/10.1016/0264-8172(94)90059-0.
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Burchette, Trevor. "Advances in reservoir geology." Sedimentary Geology 88, no. 3-4 (January 1994): 311–13. http://dx.doi.org/10.1016/0037-0738(94)90073-6.
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Chapman, Richard E. "Advances in reservoir geology." Earth-Science Reviews 34, no. 4 (August 1993): 282–83. http://dx.doi.org/10.1016/0012-8252(93)90068-i.
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Hoffman, Monty, and James Crafton. "Multiphase flow in oil and gas reservoirs." Mountain Geologist 54, no. 1 (January 2017): 5–14. http://dx.doi.org/10.31582/rmag.mg.54.1.5.
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The porous rocks that make up oil and gas reservoirs are composed of complex combinations of pores, pore throats, and fractures. Pore networks are groups of these void spaces that are connected by pathways that have the same fluid entry pressures. Any fluid movement in pore networks will be along the pathways that require the minimum energy expenditure. After emplacement of hydrocarbons in a reservoir, fluid saturations, capillary pressure, and energy are in equilibrium, a significant amount of the reservoir energy is stored at the interface between the fluids. Any mechanism that changes the pressure, volume, chemistry, or temperature of the fluids in the reservoir results in a state of energy non-equilibrium. Existing reservoir engineering equations do not address this non-equilibrium condition, but rather assume that all reservoirs are in equilibrium. The assumption of equilibrium results in incorrect descriptions of fluid flow in energy non-equilibrium reservoirs. This, coupled with the fact that drilling-induced permeability damage is common in these reservoirs, often results in incorrect conclusions regarding the potential producibility of the well. Relative permeability damage, damage that can change which fluids are produced from a hydrocarbon reservoir, can occur even in very permeable reservoirs. Use of dependent variables in reservoir analysis does not correctly describe the physics of fluid flow in the reservoir and will lead to potentially incorrect answers regarding producibility of the reservoir.
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Zhang, Xuejuan, Lei Zhang, Dandan Wang, Kuo Lan, Xuesong Zhou, Hongyu Yu, Ruhao Liu, and Xueying Lv. "Nonuniform grid upscaling method for geologic model of oil reservoir: A case study of the W block in the northern part of the Songliao Basin." Interpretation 9, no. 2 (April 7, 2021): T443—T452. http://dx.doi.org/10.1190/int-2020-0112.1.
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At present, uniform upscaling division methods are routinely used to upscale geologic model grids, resulting in overly fine grids in some areas of the model. To improve computational efficiency, we have examined the effect of model upscaling with different upscaling parameters with the goal of producing a nonuniform grid with uniform accuracy. We based our nonuniform upscaling grid method on geologic characteristics including reservoir thickness, physical properties, reservoir spacing, and water flooding. Most of the logging curves of thin reservoirs are finger-like, allowing us to define the grid size according to the reservoir thickness. We use two different strategies to discretize uniform and composite reservoirs and represent reservoir thickness that exhibit bell- and funnel-shaped logging curves. Although one grid point accurately represents a uniform reservoir, we find that composite reservoirs require four or five points to accurately represent the physical properties of a composite reservoir. For the thick reservoirs (>5 m) with box- or composite-type logging curves, the physical properties inside the reservoir do not change much; therefore, we use a grid point to represent the reservoir thickness information. Using these metrics, we constructed alternative “moderate” and “efficient” vertical grid upscaling strategies. Taking the 15 sedimentary units with a total thickness of 72 m as an example, the statistical results show that the computational efficiency using our data-adaptive grid can be increased more than five times compared to the traditional uniform fine-grid method while retaining the same accuracy.
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Ahmed, Rayan. "Geological Model for Mauddud Reservoir Khabaz Oil Field." Iraqi Geological Journal 54, no. 1D (April 30, 2021): 29–42. http://dx.doi.org/10.46717/igj.54.1d.3ms-2021-04-23.
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The Mauddud reservoir, Khabaz oil field which is considered one of the main carbonate reservoirs in the north of Iraq. Recognizing carbonate reservoirs represents challenges to engineers because reservoirs almost tend to be tight and overall heterogeneous. The current study concerns with geological modeling of the reservoir is an oil-bearing with the original gas cap. The geological model is establishing for the reservoir by identifying the facies and evaluating the petrophysical properties of this complex reservoir, and calculate the amount of hydrocarbon. When completed the processing of data by IP interactive petrophysics software, and the permeability of a reservoir was calculated using the concept of hydraulic units then, there are three basic steps to construct the geological model, starts with creating a structural, facies and property models. The reservoirs were divided into four zones depending on the variation of petrophysical properties (porosity and permeability). Nine wells that penetrate the Cretaceous Formation (Mauddud reservoir) are included to construct the geological model. Zone number three characterized as the most important due to it Is large thickness which is about 108 m and good petrophysical properties are about 13%, 55 md, 41% and 38% for porosity, permeability, water saturation and net to gross respectively. The initial oil and gas in place are evaluated to be about 981×106 STB and 400×109 SCF.
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Larter, S. R., A. C. Aplin, P. W. M. Corbett, Neil Ementon, Mei Chen, and P. N. Taylor. "Reservoir Geochemistry: A Link Between Reservoir Geology and Engineering?" SPE Reservoir Engineering 12, no. 01 (February 1, 1997): 12–17. http://dx.doi.org/10.2118/28849-pa.
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Hussein, Marwa, Robert R. Stewart, Deborah Sacrey, Jonny Wu, and Rajas Athale. "Unsupervised machine learning using 3D seismic data applied to reservoir evaluation and rock type identification." Interpretation 9, no. 2 (April 21, 2021): T549—T568. http://dx.doi.org/10.1190/int-2020-0108.1.
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Net reservoir discrimination and rock type identification play vital roles in determining reservoir quality, distribution, and identification of stratigraphic baffles for optimizing drilling plans and economic petroleum recovery. Although it is challenging to discriminate small changes in reservoir properties or identify thin stratigraphic barriers below seismic resolution from conventional seismic amplitude data, we have found that seismic attributes aid in defining the reservoir architecture, properties, and stratigraphic baffles. However, analyzing numerous individual attributes is a time-consuming process and may have limitations for revealing small petrophysical changes within a reservoir. Using the Maui 3D seismic data acquired in offshore Taranaki Basin, New Zealand, we generate typical instantaneous and spectral decomposition seismic attributes that are sensitive to lithologic variations and changes in reservoir properties. Using the most common petrophysical and rock typing classification methods, the rock quality and heterogeneity of the C1 Sand reservoir are studied for four wells located within the 3D seismic volume. We find that integrating the geologic content of a combination of eight spectral instantaneous attribute volumes using an unsupervised machine-learning algorithm (self-organizing maps [SOMs]) results in a classification volume that can highlight reservoir distribution and identify stratigraphic baffles by correlating the SOM clusters with discrete net reservoir and flow-unit logs. We find that SOM classification of natural clusters of multiattribute samples in the attribute space is sensitive to subtle changes within the reservoir’s petrophysical properties. We find that SOM clusters appear to be more sensitive to porosity variations compared with lithologic changes within the reservoir. Thus, this method helps us to understand reservoir quality and heterogeneity in addition to illuminating thin reservoirs and stratigraphic baffles.
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Biek, Robert. "Virgin Anticline and Quail Creek Reservoir." Geosites 1 (December 30, 2019): 1–8. http://dx.doi.org/10.31711/geosites.v1i1.52.
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The first thing most visitors to Quail Creek State Park notice, apart from the improbably blue and refreshing waters of the reservoir itself, are the brightly colored, layered rocks of the surrounding cliffs. In fact, Quail Creek State Park lies astride one of the most remarkable geologic features in southwestern Utah. The park lies cradled in the eroded core of the Virgin anticline, a long upwarp of folded rock that trends northeast through south-central Washington County. The fold is breached by erosion along its crest, creating a window into the geologic past. Famous for its geology, the park is also infamous for the 1989 catastrophic collapse of the Quail Creek south dike, which unleashed a torrent of water and caused millions of dollars of damage.
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Abbaszadeh, Maghsood, Chip Corbett, Rolf Broetz, James Wang, Fangjian Xue, Tom Nitka, Yong Zhang, and Zhen Yu Liu. "Development of an Integrated Reservoir Model for a Naturally Fractured Volcanic Reservoir in China." SPE Reservoir Evaluation & Engineering 4, no. 05 (October 1, 2001): 406–14. http://dx.doi.org/10.2118/74336-pa.
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Summary This paper presents the development of an integrated, multidiscipline reservoir model for dynamic flow simulation and performance prediction of a geologically complex, naturally fractured volcanic reservoir in the Shang 741 Block of the Shengli field in China. A static geological model integrates lithological information, petrophysics, fracture analysis, and stochastic fracture network modeling with Formation MicroImage (FMI) log data and advanced 3D seismic interpretations. Effective fracture permeability, fracture-matrix interaction, reservoir compartmentalization, and flow transmissibility of conductive faults are obtained by matching various dynamic data. As a result of synergy and multiple iterations among various disciplines, a history-matched dynamic reservoir-simulation model capable of future performance prediction for optimum asset management is constructed. Introduction The multidisciplinary approach of closely related teamwork across the disciplines of geology, geophysics, petrophysics, and reservoir engineering is now the accepted approach in the industry for reservoir management and field development.1–6Fig. 1 shows components of integrated reservoir characterization and the contribution of each discipline to the process. The strength of integrated reservoir modeling, however, can be particularly dramatized with some reservoirs that contain extreme forms of heterogeneity and unusual structural features. The Shang 741 Block of the Shengli fractured volcanic reservoirs is one such example. The Shang 741 Block contains a series of vertically separated fractured volcanic reservoirs with different characteristics. Matrix porosity and permeability are both low in most horizons; thus, natural fractures are the main flow pathways for fluids. FMI logs delineate the orientation and density of the fracture distribution. Lithology variations, extensive compartmentalization, and looping of reservoir body units are recognized from the geologic depositional model and seismic data. Tying acoustic well data to 3D seismic data through synthetic seismograms combined with FMI information controls time and depth structure maps for a reliable geological model. Reservoir modeling (RM) software provides a platform to integrate lithology correlations with seismically based structural features and petrophysical properties to yield a framework for a dual-porosity Eclipse** reservoir flow-simulation model. Fractures delineated and characterized from well data are stochastically distributed in the reservoir for each horizon with a fractal-based, fracture-mapping algorithm.7 Simulation of effective gridblock fracture permeability and matrix-fracture transfer function parameters are upscaled into coarse-scale simulation gridblocks. These upscaled values are verified and calibrated by available pressure-transient effective permeabilities for consistency. In this paper, a dual-porosity reservoir-simulation model is constructed from a static geological and geophysical (G&G) model in a stepwise fashion through successive incorporation of dynamic information from pressure-transient tests, static reservoir pressure, water breakthrough behavior, and well-production performance data. Compartmentalization incorporates effects of multiple oil/ water contacts (OWC) for proper modeling of regional pressure-trend behavior. Fault conductivity or thin channel transmissibility, verified by seismic and well tests, is augmented for better modeling of water movement in the reservoir. As a result of synergy among various G&G disciplines and incorporation of dynamic reservoir engineering data, a representative and production-data calibrated model is constructed for this reservoir. The paper shows that this is possible only through multiple iterations across the disciplines and through integrated project teams. The model also serves as a reservoir-management tool in production monitoring, in evaluating the effects of implementing pressure-maintenance injection programs, and in better understanding the impact of various uncertainties on the ultimate recovery of the field. Database The data sources available for this study include:Geological interpretations and geological framework model, including geological markers.Three-dimensional seismic survey data with 529 lines by 583 common depth points (CDPs) at 25-m bin size that covers a 200-km2 area.Three vertical seismic profile (VSP) surveys and their detailed interpretations.Petrophysical analysis on 13 nearly vertical wells that penetrate the reservoir horizons.FMI logs and analysis for fracture delineation.Pressure/volume/temperature (PVT) samples and analyses.Conventional and special core analysis for matrix and fracture relative permeability, matrix capillary-pressure characteristics, and rock compaction.Two single-well, pressure-buildup tests.Three interference tests.Spot static-pressure measurements.Production data, including flowing bottomhole and tubing pressure, oil, water, and gas flow rates.Extensive information from 13 drilled wells in the field. Reservoir Characterization Geology. Shang 741 fractured reservoirs are located within the large Shengli field in the Bohai basin, China (Fig. 2). These volcanic reservoirs, primarily of the Oligocene Shahejie and Dongying formations, are composed of fractured basalt, extrusive tuff, and fractured diabase of intrusive origin (Fig. 3). The Shang 741 consists of a stack of separated fractured reservoirs, which communicate with each other only through drilled wellbores. These are divided into the H1, H2, H3, Lower H3, H3 1, and H4 fractured reservoir units. Fig. 4 shows the stacking order of these reservoirs along with geological markers, lithology type, and facies relationships.
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Bessen, Ryan, Jennifer Gifford, Zack Ledbetter, Sean McGuire, Kyle True, and David Malone. "Geologic Map of the Park Reservoir Quadrangle, Sheridan County, Wyoming." Mountain Geologist 57, no. 4 (October 28, 2020): 375–88. http://dx.doi.org/10.31582/rmag.mg.57.4.375.
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This project involved the construction of a detailed geologic map of the Park Reservoir, Wyoming 7.5-Minute Quadrangle (Scale 1:24,000). The Quadrangle occurs entirely in the Bighorn National Forest, which is a popular recreation site for thousands of people each year. This research advances the scientific understanding of the geology of the Bighorn Mountains and the Archean geology of the Wyoming Province. Traditional geologic mapping techniques were used in concert with isotopic age determinations. Our goal was to further subdivide the various phases of the 2.8–3.0 Ga Archean rocks based on their rock types, age, and structural features. This research supports the broader efforts of the Wyoming State Geological Survey to complete 1:24,000 scale geologic maps of the state. The northern part of the Bighorn Mountains is composed of the Bighorn batholith, a composite complex of intrusive bodies that were emplaced between 2.96–2.87 Ga. Our mapping of the Park Reservoir Quadrangle has revealed the presence of five different Archean quartzofeldspathic units, two sets of amphibolite and diabase dikes, a small occurrence of the Cambrian Flathead Sandstone, two Quaternary tills, and Quaternary alluvium. The Archean rock units range in age from ca. 2.96–2.75 Ga, the oldest of which are the most ancient rocks yet reported in the Bighorn batholith. All the Archean rocks have subtle but apparent planar fabric elements, which are variable in orientation and are interpreted to represent magmatic flow during emplacement. The Granite Ridge tear fault, which is the northern boundary of the Piney Creek thrust block, is mapped into the Archean core as a mylonite zone. This relationship indicates that the bounding faults of the Piney Creek thrust block were controlled by weak zones within the Precambrian basement rocks.
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Wei, Qin Lian, and Ling Xiao. "The Reservoir Plane Heterogeneity Characteristics of the Number 2 of the Shanxi Formation in Changbei Gas Field, Ordos Basin, China." Advanced Materials Research 524-527 (May 2012): 81–84. http://dx.doi.org/10.4028/www.scientific.net/amr.524-527.81.
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Reservoir plane heterogeneity means the geometry，the scale，the continuity and the plan variation of physical properties of reservoirs, which is one of the main factors influencing the injection-production in oil reservoirs. Therefore, the study of the reservoir plane heterogeneity play a great role in guiding development wells deployment，gas reservoir well nets adjustment and residual oil & gas development. The reservoir heterogeneity of the sandstone size of gas and the border, and unbalanced formation pressure because of the degree of the development of each well is uneven prevent ChangBei gas field to develoment. They cause difficulty of evaluating the gas field comprehensive，level development wells deployment and well trajectory adjustment，and lead to certain geology risk. It is necessary to study the reservoir heterogeneity of the number 2 of shanxi Formation in this block for concerning the unfavourable extraction condition. The composite index of reservoir plane heterogeneity of the number 2 of shanxi Formation in ChangBei gas field have calculated by adopting entropy method considering influcing reservoir plane heterogeneity which is porosity, tight sandstone, mutation coefficient and variation coefficient of permeability, range of permeability and interlayer frequency. The distributive maps of reservoir's plane heterogeneity under the restriction of sedimentary facies have also been drawed. The entropy method can full use of the reduction and strengthen of entropy method,which means the characteristic of removing the similarities and depositing differences. The study indicate that reservoir plane heterogeneity of the number 2 of shanxi Formation in study area presents the medium to slightly strong characteristics in general.
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Wang, Li Chang, Zhi Zhang Wang, and Guo Tao. "Application of Data Mining on Reservoir." Advanced Materials Research 356-360 (October 2011): 2950–53. http://dx.doi.org/10.4028/www.scientific.net/amr.356-360.2950.
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Abstract. Domestic oil-gas fields are almost approaching production tail, and an increasing number of non-traditional oil-gas reservoirs are encountered during the process of exploratory development, which leads to a urgent requirement for an advanced method in that conventional methods, such as cross plot and multiple linear regression cannot precisely describe such complex oil-gas reservoirs. Thus, the main purpose of this paper is to come up with method of Decision Tree as final model for identification of reservoir fluid based on the comparison of advantage and disadvantage of fours methods, including Decision Tree, Support Vector Machines, Artificial Neural Network and Bayesian Network. In sum, data mining is a prospective applied method in oil reservoir geology.
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Bushell, T. P. "Reservoir Geology of the Morecambe Field." Geological Society, London, Special Publications 23, no. 1 (1986): 189–208. http://dx.doi.org/10.1144/gsl.sp.1986.023.01.12.
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D'Heur, Michel, Laurent de Walque, and Frédérique Michaud. "Geology of the Edda Field Reservoir." Marine and Petroleum Geology 2, no. 4 (November 1985): 327–40. http://dx.doi.org/10.1016/0264-8172(85)90028-5.
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Qin, Sang, Qing Ke Yu, Wu Chang Long, and Yang Tong Shui. "The Research of Reservoir Earthquake Prediction Based on the Design of Horizontal Well of Thin Carbonate Reservoir of J2 Gas Reservoir in Lingyinsi Structure." Journal of Computational and Theoretical Nanoscience 13, no. 10 (October 1, 2016): 7513–18. http://dx.doi.org/10.1166/jctn.2016.5746.
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Compared to clastic rocks, the exploration and development of thin carbonate reservoir are 1more complex. Especially, the thin reservoir demands high resolution of seismic data. So the conventional methods are difficult to good exploration at the present time. According to the complexity of thin carbonate reservoir, we comprehensively analyze reservoir geology, logging and seismic response, combine natural gamma inversion, velocity inversion with porosity inversion for prediction of reservoir and obtain precise quantitative earthquake prediction. The analysis demonstrates the reliability of seismic reservoir prediction applied to design for horizontal well. On the basis of understanding the basic geological characteristics of the reservoir, this thesis takes the design for horizontal well of J21 for example, completes two sets of designs for horizontal wells group by combining prediction technology for reservoir seismic with geologic steering technique and greatly improve the drilling rate for thin reservoir.
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Liu, Wei Fu, Shuang Long Liu, and Hong Ying Han. "Depositional Model and Development Significance of Clastic Reservoir." Applied Mechanics and Materials 522-524 (February 2014): 1245–48. http://dx.doi.org/10.4028/www.scientific.net/amm.522-524.1245.
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A general geologic sedimentation model for reservoir is made by carefully analyzing the inberent essence of depositional environmentand for clastic rocks of lake basin. The basic model in the streaming environment is composed of two basic facies units: one is the waterway facie and the other is non-waterway facie. The principal characteristics of developing geology and sedimentology have been outlined. It can be commonly used in developing under-producted reserves and raising recovery ratio in the highly developed oil fields.
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Gilchrist, Callum J. D., John W. Cosgrove, and Kevin J. Parmassar. "Critically stressed fractures: analysis of the Shaikan Field, Kurdistan Region of Iraq." Journal of the Geological Society 177, no. 6 (July 23, 2020): 1315–28. http://dx.doi.org/10.1144/jgs2019-136.
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The Shaikan Field is a large producing oil field in the Kurdistan region of Iraq. It consists of multiple fractured reservoirs consisting of limestones, calcareous sandstones and mudstones. The surrounding tectonic terrane is situated in the seismically active Zagros–Taurus orogenic zone, where present-day stresses are high. The regional stresses are found to impose conditions that satisfy failure along reservoir-bound fractures, suggesting that a significant proportion of fractures are likely to be critically stressed. The in situ maximum principal stress magnitudes are estimated using three methods, namely, the tensile and compressive strengths of reservoir rock, and leak-off test (LOT) data. Stress-field orientations are determined from wellbore image log data, which are used to interpret wellbore breakouts and the associated induced tensile fractures. Reservoir pressure has declined since production started and poroelastic responses have been assessed and used to estimate the present-day stress-state and the criticality of those fractures that are most likely to fail or slip. Although a conventional approach has been used the present authors argue that a new approach to stress response with changing pore pressure should be taken. Unlike the previous theory of criticality in which a reduction in pore pressure is considered to lead to a stabilization of the fracture network, the present study suggests that a system may remain critically stressed regardless of pressure decline.Thematic collection: This article is part of the The Geology of Fractured Reservoirs collection available at: https://www.lyellcollection.org/cc/the-geology-of-fractured-reservoirs
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Zhang, Quanpei, Tao Jiao, Hao Huang, Zhao Qi, Tao Jiang, Gang Chen, Yushuang Zhu, and Na Jia. "Pore structure and fractal characteristics of ultralow-permeability sandstone reservoirs in the Upper Triassic Yanchang Formation, Ordos Basin." Interpretation 9, no. 3 (June 30, 2021): T747—T765. http://dx.doi.org/10.1190/int-2020-0185.1.
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The complex pore structure and high heterogeneity of ultralow-permeability sandstone reservoirs have a significant effect on reservoir quality evaluation and hydrocarbon resource assessment. We collected 10 reservoir samples from the Upper Triassic Yanchang Formation Chang 8 in the Zhenbei area of the Ordos Basin. We measured the pore size distribution (PSD) and fluid occurrence characteristics of the reservoir by pressure-controlled porosimetry, rate-controlled porosimetry, and nuclear magnetic resonance, and then we analyzed the results via the fractal theory to determine the pore space fractal characteristics. Our analysis indicates that the three major pore types of these reservoirs are residual intergranular pores, dissolution pores, and intercrystalline pores. The pore structure of the ultralow-permeability sandstone reservoirs is highly heterogeneous with pore throats of various scales, and the corresponding fractal characteristics are notably different, exhibiting multifractal features. Compared with macropores and mesopores, micropores are more uniform and regular in terms of their PSD and thus only slightly influence the reservoir quality. The complexity of the throat distribution and whole pore space is attributed to the development of dissolution pores and the content of feldspar minerals. Fractal features depend on the movable fluid pore space and effective pores, whose fractal dimensions reflect the complexity of interconnected pores and correlate well with the porosity and permeability. The development of different types and sizes of pore throats in these ultralow-permeability sandstone reservoirs resulted in the observed pore structure heterogeneity. The difference in mineral composition and content of these reservoirs aggravates the pore structure complexity and affects the reservoir quality evaluation and further oilfield development.
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Dehghani, Kaveh, and Robert Ehrlich. "Evaluation of the Steam-Injection Process in Light-Oil Reservoirs." SPE Reservoir Evaluation & Engineering 4, no. 05 (October 1, 2001): 395–405. http://dx.doi.org/10.2118/73403-pa.
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Summary Feasibility of steam injection for three light-oil reservoirs in different geologic settings has been evaluated. The settings studied were a waterflooded deltaic sandstone, a waterflooded vuggy dolomite, and a deltaic sandstone structural trap with a gas cap. Optimization of steam injection to take advantage of individual reservoir characteristics is demonstrated. Results show that light-oil steamfloods can be designed to take advantage of post-secondary oil-saturation distribution. The resulting project may be carried out in a considerably different fashion from that of conventional heavy-oil steamfloods. We also re-evaluated an unsuccessful light-oil steamflood (LOSF) project carried out in the past. The re-evaluation correctly predicted failure because of early steam breakthrough. The results show that by considering details of geology and displacement process physics, the recent advances in reservoir characterization and modeling tools enable us to predict the performance of these projects more accurately. Introduction Steamflooding in shallow heavy-oil sands is a mature, successful technology with large commercial projects in the U.S., Canada, Indonesia, and Venezuela. Light-oil reservoirs have had fewer steamflood applications even where depth and other factors are favorable because of the generally lower post-secondary oil in place.1,2 Unlike heavy-oil steamflood projects, there are no large-scale commercial light-oil projects to use as analogs for design purposes. There are, however, a number of field trials in the literature, which have been reported both as successes and as failures. A review of the reported cases reveals some common reasons for success or failure. The major successful LOSF field cases are reported in Tables 1 and 2.3–10 Using steam as a heating agent in heterogeneous or extensively fractured reservoirs has given positive results (e.g., Teapot Dome field, Wyoming, U.S.A., and Lacq Supérieur field, France).9,10 Also, injecting steam into thick reservoirs with gas caps has resulted in expanding the gas cap and accelerating the gravity-drainage process (e.g., Shiells Canyon field, California, U.S.A., and Smackover field, Arkansas, U.S.A.).5,7 When steam is used for drive only, field trials have been successful in reservoirs with favorable geology (e.g., Schoonebeek field, The Netherlands, and Brea field, California, U.S.A.).3,4 The major unsuccessful11–15 LOSF field trials are shown in Table 3. One common characteristic of unsuccessful field trials has been steam channeling through thief zones (e.g., East Coalinga, California, U.S.A.; Triumph field, Pennsylvania, U.S.A.; El Dorado field, Kansas, U.S.A.; and Buena Vista field, California, U.S.A.).11–15 Scaling was another reason given for project failure (e.g., Elk Hills, California, U.S.A.).15 The overall screening criteria for the light-oil steamflood are shown in Table 4.1 All the above reservoirs met these screening criteria. Although these criteria are useful preliminary guidelines, the failed projects show that each reservoir should be examined individually. When reservoir geology and oil gravity are considered, the major recovery mechanisms in light-oil steamfloods can change significantly, as compared with conventional heavy-oil steamfloods. Viscosity reduction may increase recovery by accelerating the gravity-drainage process in thick columns for light crudes of 20 to 25°API. The beneficial effect of steam specific to lighter oils is distillation of light hydrocarbons, which results in very low residual oil saturation where steam displacement occurs, as well as enhanced solution gas drive and accelerated depletion from zones that are heated but not displaced. Because of greater initial fluid mobility in light-oil reservoirs, high steam-injection rates can be used. The light-oil steamflood projects can benefit from wider well spacing than conventional heavy-oil steamfloods. All of these can result in effective projects in a variety of geologic settings, despite lower oil in place than would be considered acceptable for heavy oils. The objective of this work was to examine the feasibility of steam-injection processes that are optimized to take maximum advantage of specific reservoir geologic settings. This study was conducted for three Chevron reservoirs:A waterflooded deltaic sandstone reservoir with channel and bar deposits.A waterflooded heterogeneous vuggy carbonate reservoir.A structural trap deltaic sandstone with a gas cap. To help validate the evaluation process, Buena Vista Hills,14,16 a failed light-oil steam field trial reported by Chevron, was also re-evaluated. Deltaic Sandstone Background. The first reservoir considered for steamflood evaluation is Minas in Central Sumatra. The reservoir is in early Miocene sandstones in the Sihapas formation at an average depth of 2,000 ft subsea with a maximum vertical oil column of 480 ft. Average porosity is 26%. The oil is 36°API with an average initial bubblepoint pressure of 235 psig. Current reservoir pressure is approximately 350 psig. Reservoir temperature is 207°F. The original oil in place (OOIP) estimate is 9 billion bbl.17,18 Initial development was on 214-acre spacing. Initial production was by aquifer drive that was augmented by peripheral water injection beginning in 1970. Starting in 1978, infilling reduced spacing to 71 acres. In the early 1990's, phased pattern waterflood development was implemented by use of 71-acre inverted seven-spot patterns. This development is approximately 70% complete and is targeted only in the thickest parts of the field. Ultimate recovery following completion is estimated to be 51% of OOIP. Geologic Setting. The Sihapas group of interbedded sandstones and shales was deposited as part of a large delta complex. Principal sand bodies are channel deposits and subtidal bar deposits. The channel deposits have an erosive base overlain by coarse sand and gravel and become finer-grained and shalier upward. The bar sands have a gradational base and become cleaner and coarser-grained upward. This is shown in Fig. 1. Steamflood Process. Both gravity and permeability contrasts will cause water to underrun in the channel sands, creating high-oil-saturation bypassed zones. Because of the natural tendency of steam to override, oil from these zones should be swept in a steamflood. It was felt that areas of the field with the greatest total thickness of these channel sand tops had the best steamflood potential.
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Badri, Mohammed, Ali Yousif, and Maged Mabrook. "Multiscale reservoir surveillance and monitoring." Leading Edge 40, no. 5 (May 2021): 383–84. http://dx.doi.org/10.1190/tle40050383.1.
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Geoscientists and reservoir engineers are challenged to integrate data of different scales to better understand fluid movement in oil reservoirs. Different technologies are capable of imaging fluid movement in the reservoir at different scales. Two-dimensional fluid imaging has been achieved recently through crosswell and surface-to-borehole electromagnetic (EM) measurements. Three-dimensional fluid movement imaging has shown potential by using surface seismic data volumes. The Multiscale Reservoir Surveillance and Monitoring Workshop, held virtually 7–9 December 2020, attempted to address the challenge of how to integrate these measurements obtained at different scales into a workflow to improve the understanding of fluid flow, which is critical for sweep efficiency and recovery.
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Primaleon, Loraine Pastoriza, Kenneth J. W. McCaffrey, and Robert E. Holdsworth. "Fracture attribute and topology characteristics of a geothermal reservoir: Southern Negros, Philippines." Journal of the Geological Society 177, no. 5 (May 5, 2020): 1092–106. http://dx.doi.org/10.1144/jgs2019-126.
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The characterization of fracture networks using attribute and topological analyses has not been widely applied to the understanding and prediction of the secondary porosity, permeability and fluid flow characteristics of geothermal resources. We acquired fracture length, aperture, intensity and topological data from remotely sensed images and surface exposures of the Cuernos de Negros region and compared these data with well cores and thin sections from the underlying active geothermal reservoir: the Southern Negros Geothermal Field, west central Philippines. We show that the fracture attributes of the analogue and reservoir are best described by a power law distribution of fracture length and aperture intensity across six to eight orders of magnitude. This characterization of outcrop and borehole fractures validates the use of surface exposures as analogues for the Southern Negros Geothermal Field reservoir rocks at depth. An observed change in the scaling exponent in the 100–500 m length scale suggests that regional to sub-regional fracture systems scale differently from those at the meso- and macroscale, which may be a strata-bound effect or a sampling issue. Topological analyses show a dominance of Y-nodes and doubly connected branches, that indicates a high degree of fracture connectivity, which is important for effective fluid flow.Supplementary Material: Slopes, coefficient of determination and Aikake information criterion values of the cumulative frequency v. length and aperture plots of all fracture transects are available at https://doi.org/10.6084/m9.figshare.c.4960559Thematic collection: This article is part of the The Geology of Fractured Reservoirs collection available at: https://www.lyellcollection.org/cc/the-geology-of-fractured-reservoirs
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Caers, J. K., S. Srinivasan, and A. G. Journel. "Geostatistical Quantification of Geological Information for a Fluvial-Type North Sea Reservoir." SPE Reservoir Evaluation & Engineering 3, no. 05 (October 1, 2000): 457–67. http://dx.doi.org/10.2118/66310-pa.
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Summary Accurate prediction of petroleum reservoir performance requires reliable models of the often complex reservoir heterogeneity. Geostatistical simulation techniques generate multiple realizations of the reservoir model, all equally likely to be drawn. Traditional to geostatistics, geological continuity is represented through the variogram. The variogram is limited in describing complex geological structures as it measures correlation between rock properties at two locations only: it is a two-point statistic. Reservoir analogs such as outcrops can serve as training images depicting the interpreted geological structure. Due to scarcity of well data, the variogram models are often borrowed from such training sets. However, the same training images could be utilized to extract more complex information in the form of multiple-point statistics measuring the joint dependency between multiple locations. This paper compares a traditional variogram-based geostatistical model vs. a novel geostatistical method utilizing multiple-point statistics borrowed from training images. The comparison is made on the basis of flow performance for a typical North Sea reservoir. To obtain such comparison a "true" reference reservoir is generated using object-based simulation that depicts the complex intertwining of fluvial channels. Next, a different but similar reservoir is generated, termed the "training reservoir." The latter is used to extract the necessary structural information, be it variograms or multiple-point statistics, to build multiple geostatistical models of the true reservoir conditioned to sparse well data. A waterflood flow scenario with an inverted five-spot pattern is simulated using ECLIPSE on the true reference and the various geostatistical models. Water breakthrough characteristics and water saturation distributions are used for comparison. Introduction Typically, geostatistical reservoir characterization must address two important issues. First, a structural model needs to be established that provides an adequate description of the underlying geology. In geostatistics, the structural model describes the spatial continuity of geology in all directions. Traditional to geostatistics is to take variogram(s) as the basis for that prior to the structural model. Second, the structural model needs to be conditioned to all available hard and soft data. The intent of this paper is to compare two approaches to reservoir modeling: a traditional variogram-based technique and a novel training image-based simulation method. In traditional geostatistics, one models the variogram from well data, then one produces simulation models that honor or reflect the variogram model. This seems a highly objective procedure: the variogram model, which conditions the pattern generated from the reservoir model originates from data from the same reservoir. However, the practice of geostatistics has shown that it is difficult to model variograms from the limited well data and the variogram is often borrowed from ancillary information such as outcrops. Moreover, it is by now understood that the variogram is a very limited measure for quantifying spatial patterns. Every simulation algorithm that is variogram based implicitly needs to assume higher-order statistics (e.g., Gaussian simulation methods1). Essentially, any simulation algorithm imposes higher-order statistics beyond the control of the reservoir modeler. These imposed higher-order statistics, termed multiple-point statistics, might conflict with the actual understanding of the reservoir geology. The novel approach presented is based on the fact that outcrop or any other source of ancillary geological information allows us to borrow spatial structures beyond the variogram, which is only a two-point correlation measure. These patterns are borrowed in the form of multiple-point statistics from so-called training images, allowing a better description of the complex reservoir geology. Such training images could be as simple as a series of hand-drawn sketches by the geologist or a compilation of outcrop data (there may be several at different scales). If enough ancillary geological information is present, it should be possible to construct three-dimensional (3D) training images. If not enough geological information is present one can resort back to the traditional variogram-based method. Although the proposed methodology is general, this paper shows the application of the novel approach to a North Sea reservoir dataset and attempts to make comparisons with the variogram-based methodology. The comparison is based on the flow performance of a set of reservoir models generated with each geostatistical technique. The geology of many North Sea reservoirs is very heterogeneous due to the presence of high-permeability fluvial channels.2 The amount of hard data available along wells is typically sparse and the soft data (seismic) show a low correlation with petrophysical or facies properties within the reservoir. Hence, the construction of a representative prior structural model, accurately representing the reservoir geology, is of crucial importance. Data Sets The reservoir under study is a Triassic fluvial reservoir typical of a large number of fields in the North Sea. The fluvial channel formation was deposited by streams that range from braided to low-moderate sinuosity. The reservoir is made up of complex patterns of sand intercalated in a silty mudstone matrix. The reservoir is characterized by a trend of upward increasing sandiness. Well-defined fluvial channels of sandstones embedded in a mudstone matrix occur towards the base, while interstratified channels occur towards the top. For more-detailed information about the geology of such reservoirs refer to Ref. 2 and 3. True Reservoir. The purpose of this study is to evaluate the impact of alternative geostatistical reservoir models on the result of a flow simulation in a setting approaching a real case. To provide a common reference, a true reservoir, whose properties are exhaustively known at each gridblock, must be established. In order to keep the number of variables limited and be able to make a conclusive comparison, the reservoir is described by two facies only: channel sand and mudstone facies. For the true reservoir, a Boolean (object-based) simulation of channels was constructed from a detailed geological description of the channeling in actual North Sea reservoirs. For more details on the Boolean algorithm used, see Ref. 4. Selected slices of the true reservoir are shown in Fig. 1. The reservoir has the following general characteristics:The reservoir is discretized into 37×66×15 gridblocks in the x, y, and z vertical directions, respectively.
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Sayer, Zoë, Jonathan Edet, Rob Gooder, and Alexandra Love. "The Machar Field, Block 23/26a, UK North Sea." Geological Society, London, Memoirs 52, no. 1 (2020): 523–36. http://dx.doi.org/10.1144/m52-2018-45.
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AbstractMachar is one of several diapir fields located in the Eastern Trough of the UK Central North Sea. It contains light oil in fractured Cretaceous–Danian chalk and Paleocene sandstones draped over and around a tall, steeply-dipping salt diapir that had expressed seafloor relief during chalk deposition. The reservoir geology represents a complex interplay of sedimentology and evolving structure, with slope-related redeposition of both the chalk and sandstone reservoirs affecting distribution and reservoir quality. The best reservoir quality occurs in resedimented chalk (debris flows) and high-density turbidite sandstones. Mapping and characterizing the different facies present has been key to reservoir understanding.The field has been developed by water injection, with conventional sweep in the sandstones and imbibition drive in the chalk. Although the chalk has high matrix microporosity, permeability is typically less than 2 mD, and fractures are essential for achieving high flow rates; test permeabilities can be up to 1500 mD. The next phase of development is blowdown, where water injection is stopped and the field allowed to depressurize. This commenced in February 2018.
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Kaufman, R. L., C. S. Kabir, B. Abdul-Rahman, R. Quttainah, H. Dashti, J. M. Pederson, and M. S. Moon. "Characterizing the Greater Burgan Field With Geochemical and Other Field Data." SPE Reservoir Evaluation & Engineering 3, no. 02 (April 1, 2000): 118–26. http://dx.doi.org/10.2118/62516-pa.
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Summary This paper describes recent results from an ongoing geochemical study of the supergiant Greater Burgan field, Kuwait. Oil occurs in a number of vertically separated reservoirs including the Jurassic Marrat reservoir and Cretaceous-Minagish, -Third Burgan, -Fourth Burgan, -Mauddud, and -Wara reservoirs. The Third and Fourth Burgan sands are the most important producing reservoirs. Over 100 oils representing all major producing reservoirs have been analyzed using oil fingerprinting as the principal method, but also supported by gravity, sulfur, and pressure-volume-temperature (PVT) measurements. From a reservoir management perspective, an important feature of the field is the approximately 1,200-ft-long hydrocarbon column which extends across the Burgan and Wara reservoirs. Oil composition varies with depth in this thick oil column. For example, oil gravity varies in a nonlinear fashion from about 10°API near the oil/water contact to about 39°API at the shallowest Wara reservoir. This gravity-depth relationship makes identification of reservoir compartments solely from fluid property data difficult. Including oil geochemistry in the traditional mix of PVT and production logging data improves the understanding of compartmentalization and fluid flow in the reservoir, both in a vertical and lateral sense. The composition of reservoir fluids is controlled by a number of geological and physical processes. We attempted to identify unique sets of geochemical parameters that were sensitive to specific oil alteration processes. One set of geochemical properties correlated strongly with gravity and is, therefore, related to the gravity-segregation process. A second set of parameters showed essentially no correlation with gravity or depth but established unique oil fingerprints for most of the major producing reservoirs and identified a number of different oil groups within the Burgan and Wara reservoirs. We interpret the presence of these oil groups to indicate reservoir compartments owing to laterally continuous shales and faults which act as seals on a geologic time frame. More tentative is the identification of production time frame barriers from the fluid composition data. The oil fingerprint data have been used to distinguish oils from the major producing reservoirs and evaluate hydrocarbon continuity within the reservoirs. Introduction This article describes a geochemical study of oils from the Greater Burgan field, Kuwait. During this study, we examined the compositional variation of oils within the field to evaluate reservoir continuity. This study is part of a larger project to describe the producing characteristics of the major reservoirs in the Burgan field en route to applying the best practices in the overall reservoir management program. In Phase I of this study,1 approximately 60 oils from the Burgan, Magwa, and Ahmadi areas of the Greater Burgan field were analyzed using oil fingerprinting. The objective was to determine if oils from the Wara, Third Burgan, and Fourth Burgan reservoirs had unique oil fingerprints and to evaluate oil mixing because of wellbore communications. In Phase II, a larger suite of wells was sampled to broaden the coverage of the field, both areally and stratigraphically, as shown in Fig. 1. Even though a considerably larger number of wells were sampled in Phase II, the sampling density still remains rather coarse in this supergiant field, spanning 320 sq mile. A variety of different techniques are available for reservoir geochemistry studies.2 The principle method used in this study is whole-oil gas chromatography; sometimes referred to as oil fingerprinting. This method has been described before3 and is, therefore, summarized only briefly here. Oil samples were collected at the wellhead, at atmospheric conditions, and analyzed using capillary gas chromatography. A standard of about 200 calibrated peak heights was developed and from this about 30 standard peak height ratios were calculated. These ratios were selected based on their ability to separate the oils into uniquely different groups. Two different multivariate statistical techniques were used to analyze the chromatography data: cluster analysis and principal components analysis. Both techniques were used to identify groups of similar oils based on the peak height ratios. Petroleum is a very complex natural product whose composition is controlled by various geologic processes which occur both before and after fluid accumulation. In our geochemical studies of the Burgan field, we have used the composition of the produced oil to study the hydrocarbon connectivity of different reservoirs. Some measurements, such as oil gravity, gas/oil ratio and bubblepoint data, characterize the bulk properties of the fluid. Other measurements, such as the hydrocarbon fingerprint, are based on the molecular composition of the fluid. Both types of data are necessary to completely characterize a petroleum reservoir, but the molecular composition data are frequently a more sensitive measure of the reservoir connectivity. Where available, both types of data have been used in this study of the Burgan field. The identification of reservoir compartments, both vertical and lateral, is a necessary component of efficient reservoir appraisal and management. Reservoirs are compartmentalized when barriers to fluid flow are present which prevent fluid communication between different parts of the reservoir. Smalley and Hale have discussed the need for early identification of reservoir compartments well in advance of dynamic production measurements.4 Some barriers are effective on a geologic time scale and frequently result in separate oil pools with unique oil/water contacts and initial pressure gradients. Other barriers may become effective on a production time frame. These are typically identified only after the field is put on production. Reservoir fluid composition data have most frequently been interpreted as indicators of geologic time-frame compartments, but it may provide an early indication of production time-frame compartments in some cases. The Greater Burgan Field The Greater Burgan oil field lies within the Arabian basin in the state of Kuwait. General reviews of the geology and producing history of the field are described by Brennan,5 Kirby et al.,6 and Carman.7 The field is subdivided into the Burgan, Magwa, and Ahmadi sectors based on the presence of three structural domes as shown in Fig. 1. The boundary between the northern Magwa/Ahmadi and the Burgan sectors is the Central Graben fault complex, as shown in Fig. 2.
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Radwan, Ahmed E. "Integrated reservoir, geology, and production data for reservoir damage analysis: A case study of the Miocene sandstone reservoir, Gulf of Suez, Egypt." Interpretation 9, no. 4 (August 4, 2021): SH27—SH37. http://dx.doi.org/10.1190/int-2021-0039.1.
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Reservoir damage is considered one of the major challenges in the oil and gas industry. Many studies have been conducted to understand formation damage mechanisms in borehole wells, but few studies have been conducted to analyze the data to detect the source, causes, and mitigations for each well where damage has occurred. I have investigated and quantified the reasons and mitigation of reservoir damage problems in the middle Miocene reservoir within the El Morgan oil field at the southern central Gulf of Suez, Egypt. I used integrated production, reservoir, and geologic data sets and their history during different operations to assess the reservoir damage in El Morgan-XX well. The collected data include the reservoir rock type, fluid, production, core analysis, rock mineralogy, geology, water chemistry, drilling fluids, perforations depth intervals, workover operations, and stimulation history. The integration of different sets of data gave a robust analysis of reservoir damage causes and helps to suggest suitable remediation. Based on these results, I conclude the following: (1) Workover fluid has been confirmed as the primary damage source, (2) the reservoir damage mechanisms could be generated by multisources including solids and filtrate invasions, fluid/rock interaction (deflocculating of kaolinite clay), water blockage, salinity chock, and the high sulfate content of the invaded fluid, and (3) multidata integration leads to appropriate reservoir damage analysis and effective design of the stimulation treatment. Furthermore, minimizing fluid invasion into the reservoir section by managing the overbalance during drilling and workover operations could be very helpful. Fluid types and solids should be considered when designing the stimulation treatment and compatibility tests should be performed. Long periods of completion fluid in boreholes are not recommended, particularly if the completion fluid pressure and reservoir pressure are out of balance, as well as the presence of sensitive formation minerals.
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Hardage, Bob A., Raymond A. Levey, Virginia Pendleton, James Simmons, and Rick Edson. "A 3-D seismic case history evaluating fluvially deposited thin‐bed reservoirs in a gas‐producing property." GEOPHYSICS 59, no. 11 (November 1994): 1650–65. http://dx.doi.org/10.1190/1.1443554.
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We conducted a study at Stratton Field, a large Frio gas‐producing property in Kleberg and Nueces Counties in South Texas, to determine how to best integrate geophysics, geology, and reservoir engineering technologies to detect thin‐bed compartmented reservoirs in a fluvially deposited reservoir system. This study documents that narrow, meandering, channel‐fill reservoirs as thin as 10 ft (3 m) and as narrow as 200 ft (61 m) can be detected with 3-D seismic imaging at depths exceeding 6000 ft (1800 m) if the 3-D data are carefully calibrated using vertical seismic profile (VSP) control. Even though the 3-D seismic images show considerable stratigraphic detail in the interwell spaces and indicate where numerous thin‐bed compartment boundaries could exist, the seismic images cannot by themselves specify which stratigraphic features are the flow barriers that create the reservoir compartmentalization. However, when well production histories, reservoir pressure histories, and pressure interference tests are incorporated into the 3-D seismic interpretation, a compartmentalized model of the reservoir system can be constructed that allows improved development drilling and reservoir management to be implemented. This case history illustrates how realistic, thin‐bed, compartmented reservoir models result when geologists, engineers, and geophysicists work together to develop a unified model of a stratigraphically complex reservoir system.
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Price, Neil, Paul LaPointe, Kevin Parmassar, Chunmei Shi, Phil Diamond, Aleta Finnila, and Ole Krogh Jensen. "Dynamic calibration of the Shaikan Jurassic full-field fractured reservoir model through single-well DST and multi-well interference discrete fracture network simulation." Journal of the Geological Society 177, no. 6 (June 15, 2020): 1294–314. http://dx.doi.org/10.1144/jgs2019-137.
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The hydraulic behaviour of the fractures in a fractured carbonate reservoir is a function of fracture intensity, aperture, intrinsic permeability, length, height and orientation, all of which influence the scale of connectivity and ultimately storage, productivity and reserves. If a geologically realistic fracture model is not appropriately incorporated into upscaled fracture properties for a dynamic simulation, it may still be possible to match a short production history, but calculations of field-wide fracture pore volumes and forecasts of future reservoir development may be poor and uncertain. To accurately represent the fractures, discrete fracture network (DFN) models were built and used to constrain fracture geometries and their hydraulic properties for use in forecasting, field development options and uncertainty characterization. The workflow illustrated in this paper shows how a DFN may be validated and calibrated through the simulation of transient bottom hole pressures from individual drill stem tests and pressure interference data, followed by upscaling to a full-field dynamic simulation model. This DFN-to-simulation workflow, applicable to most conventional fractured reservoirs, successfully matched reservoir pressure history for the field as a whole and for individual wells without having to locally modify any of the upscaled fracture properties around the wells. Sensitivity analysis identified key fracture drivers having the greatest impact upon the history match, and these were combined to produce history matched Low and High Case models. Production forecasts for the Low, Base and High Cases were used to predict reserves, manage risk and optimize the field development plan.Supplementary material: Supplementary figures are available at https://doi.org/10.6084/m9.figshare.c.5001203Thematic collection: This article is part of the The Geology of Fractured Reservoirs collection available at: https://www.lyellcollection.org/cc/the-geology-of-fractured-reservoirs
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Zouari, Mehdi, Maria Perez, Jianxiong Chen, Allison Kimbrough, Lauren Salathe, and Namrita Gandhi. "Value of a second seismic monitor in late stages of field development at Holstein." Leading Edge 39, no. 1 (January 2020): 53–61. http://dx.doi.org/10.1190/tle39010053.1.
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A second seismic monitor survey acquired after nine years of production at Holstein Field was used successfully to define new wellbore sidetracks to target unswept areas in two of the main producing reservoirs (J2 and K1). The asset team and the technology group worked together to investigate the quality and interpretability of the 4D signal between the first and second monitor surveys. The method consisted of conducting a quality check of the acquisition and processing steps, modeling the amplitude variation with offset responses using existing wells to determine the response to fluid effects, and finally extracting and creating amplitude difference maps between monitor surveys for each reservoir. The interpretation of the 4D amplitude differences, combined with the analysis of production and pressure data from historical injector and producer wells, resulted in the decision to target what was interpreted to be a partially swept J2/K1 reservoir compartment by the aquifer in the southern part of the field. Well #12 was drilled in that target and encountered oil pay in both reservoirs, with low levels of water saturation. Another J2 area in the northern part of the field was interpreted to have remained partially unswept by water injectors, although seismic acoustic softening over that portion of the field suggested that it was still benefiting from injection pressure support. Well #11 was drilled in that northern portion of the field and encountered an oil-bearing reservoir with water saturation near preproduction levels and a reservoir pressure approaching original reservoir pressure, hence confirming repressurization.
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LI, Yang, Shenghe WU, Jiagen HOU, and Jianmin LIU. "Progress and prospects of reservoir development geology." Petroleum Exploration and Development 44, no. 4 (August 2017): 603–14. http://dx.doi.org/10.1016/s1876-3804(17)30069-1.
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Wendao, Q., Y. Taiju, Zh Changmin, H. Guowei, H. Miao, X. Min, Y. Xiujin, and Y. Lan. "Geology Prediction Techniques for Reservoir Evolution Simulation." Geotectonics 53, no. 3 (May 2019): 399–418. http://dx.doi.org/10.1134/s0016852119030099.
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Deshmukh, Soumen, Rajesh Sharma, Manisha Chaudhary, and Harilal. "Integrated 3D geomechanical modeling and its application for well planning in Bantumilli South area, Krishna-Godavari Basin, India." Leading Edge 39, no. 3 (March 2020): 182–87. http://dx.doi.org/10.1190/tle39030182.1.
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Complex geologic structure, a heterogeneous reservoir, and complications related to high pressure during drilling necessitate carrying out geomechanical modeling to understand the physical properties of rocks and fluids present within the Early Cretaceous synrift sequence in the Bantumilli South area of the Krishna-Godavari Basin in India. Reservoirs within the synrift sequence exhibit low permeability and high pore pressure. Identification of safe mud-weight window zones is critical for safe drilling of wells in this part of the basin. A detailed workflow for building a robust 3D geomechanical model and its applications to well planning and hydraulic fracturing are presented. Elastic properties of the reservoirs were estimated by prestack seismic inversion. Elastic properties and pore pressure volumes were used to simulate the 3D stress field. The maximum horizontal stress direction is observed to be 130°N ± 5°, i.e., northwest to southeast, and estimated fracture pressure (minimum horizontal stress) values range between 10,000 and 14,200 psi within the synrift sequence. The study has shown that the Cretaceous section of the reservoir has narrow mud-weight window zones. These zones are governed mainly by a high pore pressure regime in the reservoirs. Additionally, deep-seated basement faults have played an important role in the compartmentalization of the reservoir in terms of geomechanical properties.
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Thorpe, D., M. Porter, T. McKie, and L. J. Ritchie. "The Penguins Cluster, Blocks 211/13a and 211/14, UK North Sea." Geological Society, London, Memoirs 52, no. 1 (2020): 916–27. http://dx.doi.org/10.1144/m52-2018-71.
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AbstractThe Penguins Cluster of fields are owned jointly (50:50) by Shell UK Ltd (Shell) and Esso Exploration and Production UK Ltd (Esso), with Shell as the operator. The cluster was discovered in 1974 and is composed of a combination of oil and gas condensate accumulations located 50–65 km north of the Brent Field, at the northern end of the North Viking Graben. Two main producing reservoirs are present: the Penguins West Field (Penguin A) consists of an Upper Jurassic Magnus Sandstone Member reservoir, while the Penguins East Field (Penguin C, D and E) consists of a Middle Jurassic Brent Group reservoir, underlain by currently undeveloped Statfjord and Triassic (Cormorant) reservoirs. The Magnus reservoir is composed of turbidite sands with an average porosity of 15% and permeabilities of 0.10–300 mD. The Brent reservoirs are composed of deltaic shoreface deposits with an average porosity of 14% and permeabilities of 0.01–1000 mD.The fields were brought on stream in 2003 as a subsea development via what at the time was the world's longest comingled tieback to the Brent Charlie facility. A total of nine producing wells have been drilled from four subsea manifolds, producing c. 78 MMboe to date through depletion drive.
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Hussein, Marwa, Robert R. Stewart, Deborah Sacrey, David H. Johnston, and Jonny Wu. "Unsupervised machine learning for time-lapse seismic studies and reservoir monitoring." Interpretation 9, no. 3 (July 1, 2021): T791—T807. http://dx.doi.org/10.1190/int-2020-0176.1.
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Time-lapse (4D) seismic analysis plays a vital role in reservoir management and reservoir simulation model updates. However, 4D seismic data are subject to interference and tuning effects. Being able to resolve and monitor thin reservoirs of different quality can aid in optimizing infill drilling or in locating bypassed hydrocarbons. Using 4D seismic data from the Maui field in the offshore Taranaki Basin of New Zealand, we generate typical seismic attributes sensitive to reservoir thickness and rock properties. We find that spectral instantaneous attributes extracted from time-lapse seismic data illuminate more detailed reservoir features compared with those same attributes computed on broadband seismic data. We have developed an unsupervised machine-learning workflow that enables us to combine eight spectral instantaneous seismic attributes into single classification volumes for the baseline and monitor surveys using self-organizing maps (SOMs). Changes in the SOM natural clusters between the baseline and monitor surveys suggest production-related changes that are caused primarily by water replacing gas as the reservoir is being swept under a strong water drive. The classification volumes also facilitate monitoring water saturation changes within thin reservoirs (ranging from very good to poor quality) as well as illuminating thin baffles. Thus, these SOM classification volumes indicate internal reservoir heterogeneity that can be incorporated into reservoir simulation models. Using meaningful SOM clusters, geobodies are generated for the baseline and monitor SOM classifications. The recoverable gas reserves for those geobodies are then computed and compared with production data. The SOM classifications of the Maui 4D seismic data seem to be sensitive to water saturation change and subtle pressure depletions due to gas production under a strong water drive.
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Struijk, A. P., and R. T. Green. "The Brent Field, Block 211/29, UK North Sea." Geological Society, London, Memoirs 14, no. 1 (1991): 63–72. http://dx.doi.org/10.1144/gsl.mem.1991.014.01.08.
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AbstractThe Brent Field was the first discovery in the northern part of the North Sea, and is one of the largest hydrocarbon accumulations in the United Kingdom licence area. There are two separate major accumulations: one in the Middle Jurassic (Brent Group reservoir) and one in the Lower Jurassic/Triassic (Statfjord Formation reservoir). The field lies entirely within UK licence Block 211/29 at latitude 61°N and longitude 2°E. The water depth is 460 ft. The discovery well was drilled in 1971, and six further exploration and appraisal wells were drilled. Seismic data over the Brent Field has been acquired in three separate vintages. The latest acquisition is a 3-D grid recorded in 1986. Reprocessing of the entire 1986 3-D seismic data set was initiated in 1989.The original oil/condensate-in-place, estimated on 1/1/89, is 3500 MMBBL, and the estimated original wet gas-in-place is 6700 TCF. Oil production is now in the decline phase. Average production in 1988 was 334,000 BOPD, with gas sales remaining at the plateau rate of 500 MMSCFD.The field is being developed from four fixed platforms, each providing production, water injection and gas injection facilities for both Brent and Statfjord Formation reservoirs. Gas injection is distributed to achieve an intermediate oil rim development in some reservoir units. The platforms were installed between 1975 and 1978. Production commenced in 1976. The slump faulted crestal areas of both reservoirs have yet to be developed.These crestal areas contain about 5% of the recoverable reserves. Appraisal drilling was carried out in the crest during 1988 and 1989.The Brent Field is located approximately 100 miles north-east of the Shetland Islands and 300 miles NNE of Aberdeen (Fig. 1). The discovery well location is at latitude 61°05'53.87" North longitude 1°41'30.H" East. The water depth is 460 ftThe field comprises two distinct reservoirs, the Brent Group and the Statfjord Formation, which are of Middle Jurassic and Lower Jurassic/Triassic age respectively. The reservoirs occur in a westerly dipping tilted fault block in a fault controlled unconformity trap (Fig. 2).The size of the hydrocarbon bearing area is approximately 10 miles from north to south and 3 miles from east to west (Fig. 3).The reservoirs are in turn divided into seven separate reservoir units; four cycles in the Brent Group reservoir and three units in the Statfjord Formation reservoir. Laterally two major east-west orientated faults divide the field into three separate production areas. A fourth area is the north-south orientated crestal part of both reservoirs, which is faulted and has a series of down faulted slump blocks overlain by 'Reworked Sediment'. This area still has to be developed.The Shell/Esso joint venture North Sea oilfields are named after water and waterside birds. The Brent Field is named after the Brent goose.
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Zhang, Ji Cheng, Xiao Yun Li, Jian Cheng Wei, and Zi Yi Xu. "The Research on Main Controlling Factors of Edge Water Invasion in Heavy Oil Thermal Recovery." Applied Mechanics and Materials 448-453 (October 2013): 4009–14. http://dx.doi.org/10.4028/www.scientific.net/amm.448-453.4009.
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The Characteristics of heavy oil reservoir and the influence factors of edge water invasion are researched though the research of summary of geology in the BQ57 area. 12 factors are confirmed with the orthogonal experiment. The research contains 12 factors,among them, geologic factors contain reservoir heterogeneity, effective thickness, Crude oil properties, angle of bedding and Edge water energy[1]; Development factors contain temperature field, Steam injection intensity, Steam injection time, soak time, Steam injection temperature, Steam dryness, production factor, and it calculates the weight of various factors.
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Chen, H. K., T. Robinson, S. D. Harker, and C. E. Maher. "The Main Area Claymore Reservoir: A Review of Geology and Reservoir Management." SPE Formation Evaluation 4, no. 02 (June 1, 1989): 231–38. http://dx.doi.org/10.2118/16556-pa.
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Liu, Q., H. Xu, Z. Lei, Z. Li, Y. Xiong, S. Li, B. Luo, and D. Chen. "Fault Mesh Petroleum Plays in the Donghetang Area, Tabei Uplift, Tarim Basin, Northwestern China, and Its Significance for Hydrocarbon Exploration." Russian Geology and Geophysics 62, no. 07 (July 1, 2021): 808–27. http://dx.doi.org/10.2113/rgg20183939.
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Abstract —The hydrocarbon formation mechanism and potential targets in clastic strata from the Tabei Uplift, Tarim Basin, are documented using the fault mesh petroleum plays theory, based on integrating seismic, well log, well core, and geochemical data. The reservoirs in the Donghetang area are typical allochthonous and far-source fault mesh petroleum plays. There are two sets of fault meshes in the study area: (1) the combination of the Donghe sandstone and Permian–Triassic strata and (2) the combination of the fourth and third formations in the Jurassic strata. The fault mesh petroleum play in the Jurassic is a secondary reservoir that originates from the Carboniferous Donghe sandstone reservoir adjustment based on source correlation. The fault mesh carrier systems show the fully connected, fault–unconformity–transient storage relay, fault–transient storage–unconformity relay, and transient storage–fault relay styles, according to the architecture of the fault mesh. Based on the characteristics of the fault mesh petroleum plays, the reservoirs are divided into three categories (upper-, inner-, and margin-transient storage styles) and 15 styles. Integrated analysis of the hydrocarbon generation and faulting time periods reveals that there were four periods of hydrocarbon charging, with the first three stages charging the reservoirs with oil and the last stage charging the reservoirs with gas. There are multiple stages of reservoir accumulation and adjustment in the fault mesh in the study area. These stages of fault mesh accumulation and adjustment are the main reason why the reservoir distribution multiple vertical units have different hydrocarbon properties. Fault-block and lithologic reservoirs related to the inner- and upper-transient storage styles are the main exploration targets in the clastic strata in the study area.
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Gumrah, F., A. Aliyev, C. Guliyeva, and O. Ozavci. "Determining reservoir characteristics and drive mechanisms for an oil reservoir." "Proceedings" of "OilGasScientificResearchProjects" Institute, SOCAR, no. 4 (December 30, 2012): 6–19. http://dx.doi.org/10.5510/ogp20120400129.
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Bray, Matthew, Jacob Utley, Yanuri Ning, Angela Dang, Jacquelyn Daves, Isabel White, Ahmed Alfataierge, et al. "Multidisciplinary analysis of hydraulic stimulation and production effects within the Niobrara and Codell reservoirs, Wattenberg Field, Colorado — Part 2: Analysis of hydraulic fracturing and production." Interpretation 9, no. 4 (July 12, 2021): SG13—SG29. http://dx.doi.org/10.1190/int-2020-0153.1.
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Enhanced hydrocarbon recovery is essential for continued economic development of unconventional reservoirs. We have focused on dynamic characterization of the Niobrara and Codell Formations in Wattenberg Field through the development and analysis of a full integrated reservoir model. We determine the effectiveness of the hydraulic fracturing and production with two seismic monitor surveys, surface microseismic, completion data, and production data. The two monitor surveys were recorded after stimulation and again after two years of production. Identification of reservoir deformation due to hydraulic fracturing and production improves reservoir models by mapping nonstimulated and nonproducing zones. Monitoring these time-variant changes improves the prediction capability of reservoir models, which in turn leads to improved well and stage placement. We quantify dynamic reservoir changes with time-lapse P-wave seismic data using prestack inversion and velocity-independent layer stripping for velocity and attenuation changes within the Niobrara and Codell reservoirs. A 3D geomechanical model and production data are history matched, and a simulation is run for two years of production. Results are integrated with time-lapse seismic data to illustrate the effects of hydraulic fracturing and production. Our analyses illustrate that chalk facies have significantly higher hydraulic fracture efficiency and production performance than marl facies. In addition, structural and hydraulic complexity associated with faults generate spatial variability in a well’s total production.
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Maleki, Masoud, Shahram Danaei, Felipe Bruno Mesquita da Silva, Alessandra Davolio, and Denis José Schiozer. "Stepwise uncertainty reduction in time-lapse seismic interpretation using multi-attribute analysis." Petroleum Geoscience 27, no. 3 (February 25, 2021): petgeo2020–087. http://dx.doi.org/10.1144/petgeo2020-087.
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Recently, time-lapse seismic (4D seismic) has been steadily used to demonstrate the relation between field depletion and 4D seismic response, and, subsequently, to provide more efficient field management. A key component of reservoir monitoring is the knowledge of fluid movement and pressure variation. This information is vital in assisting infill drilling and as a reliable source of data to update reservoir models, and, consequently, in helping to improve model-based reservoir management and decision-making processes. However, in practice, varying levels of uncertainty are inherent in the 4D seismic interpretation of reservoirs that uses a multipart production regime. The complex nature of some 4D seismic signals emphasizes the role of the competing effects of geology, rock and fluid interactions. Hence, a reliable 4D interpretation requires an interdisciplinary approach that entails data analysis and insights from geophysics, engineering and geology. In this study, a stepwise workflow was introduced to reduce the uncertainties in the 4D seismic interpretation and to identify the improvements required in order to perform better reservoir surveillance. In parallel, the workflow demonstrates the use of engineering data analysis in conducting a consistent interpretation, and encompasses the 3D and 4D seismic attributes with engineering data analysis. This study was carried out in a Brazilian heavy-oil offshore field where production started in 2013. The field experienced intense production activity up to 2016, making the deep-water field an ideal candidate to explore the challenges in interpreting complex 4D signals. Beyond these challenges, a significant understanding of reservoir behaviour is obtained and improvements to the reservoir simulation model are suggested that could assist reservoir engineers with data assimilation applications.
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Huang, Shu Jun, Hui Zhang, Cui Juan Shang, and Shu Long Jing. "Key Technologies of Rebuilding Underground Natural Gas Storages from Carbonate Buried Hill Gas Reservoirs." Advanced Materials Research 347-353 (October 2011): 1561–67. http://dx.doi.org/10.4028/www.scientific.net/amr.347-353.1561.
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In this paper, according to research difficulties of rebuilding underground natural gas storages from carbonate buried hill gas reservoirs, we select a variety of relevant technologies and methods to study. Considering the reservoir geologic features geology, the impact of water intrusion, the difference of reserve calculations and many other factors, we carry out the research and determine the key parameters of rebuilding underground natural gas storages, and finally get a reasonable understanding of the study. Upon completion of large-scale gas storage for research results, further to form the distinctive key technologies of rebuilding underground natural gas storages from carbonate buried hill gas reservoirs. The research results will provide the appropriate technical reference for similar future rebuilding underground gas storages and also provide the technical assurance for a safe and stable gas supply to Beijing, Tianjin and Hebei region.
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Huang, Zhi Quan, Tai Li Chen, and An Ming Wang. "Main Engineering Geological Problems and Evaluation on the Reservoir Engineering of Yellow River Water Diversion and Irrigation Area." Applied Mechanics and Materials 580-583 (July 2014): 2071–73. http://dx.doi.org/10.4028/www.scientific.net/amm.580-583.2071.
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Engineering geology survey was carried out on the reservoir engineering of Yellow River water diversion and irrigation area, main engineering geology problem including leakage problem of reservoir area、shore stable and siltation problem around the reservoir、siltation problem of storehouse district, connected the river course and the pilot, water leakage river course、earthquake liquefication and immerse and salinification were analyzed and appraised, the corresponding project measure was proposed against the infiltrates of the storehouse district.
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Gerami, Shahab, and Mehran Pooladi-Darvish. "An Early-Time Model for Drawdown Testing of a Hydrate-Capped Gas Reservoir." SPE Reservoir Evaluation & Engineering 12, no. 04 (July 19, 2009): 595–609. http://dx.doi.org/10.2118/108971-pa.
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Summary Development of natural gas hydrates as an energy resource has gained significant interest during the past decade. Hydrate reservoirs may be found in different geologic settings including deep ocean sediments and arctic areas. Some reservoirs include a free-gas zone beneath the hydrate and such a situation is referred to as a hydrate-capped gas reservoir. Gas production from such a reservoir could result in pressure reduction in the hydrate cap and endothermic decomposition of hydrates. Well testing in conventional reservoirs is used for estimation of reservoir and near-wellbore properties. Drawdown testing in a hydrate-capped gas reservoir needs to account for the effect of gas from decomposing hydrates. This paper presents a 2D (r,z) mathematical model for a constant-rate drawdown test performed in a well completed in the free-gas zone of a hydrate-capped gas reservoir during the earlytime production. Using energy and material balance equations, the effect of endothermic hydrate decomposition appears as an increased compressibility in the resulting governing equation. The solution for the dimensionless wellbore pressure is derived using Laplace and finite Fourier cosine transforms. The solution to the analytical model was compared with a numerical hydrate reservoir simulator across some range of hydrate reservoir parameters. The use of this solution for determination of reservoir properties is demonstrated using a synthetic example. Furthermore, the solution may be used to quantify the contribution of hydrate decomposition on production performance. Introduction In recent years, demands for energy have stimulated the development of unconventional gas resources, which are available in enormous quantities around the world. Gas hydrate as an unconventional gas resource may be found in two geologic settings (Sloan 1991):on land in permafrost regions, andin the ocean sediments of continental margins. During the last decade, extensive efforts consisting of detection of the hydrate-bearing areas, drilling, logging, coring of the intervals, production pilot-testing, and mathematical modeling of hydrate reservoirs have been pursued to evaluate the potential of gas production from these gas-hydrate resources.
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Kumar, Abhash, William Harbert, Richard Hammack, Erich Zorn, Alexander Bear, and Timothy Carr. "Evaluating proxies for the drivers of natural gas productivity using machine-learning models." Interpretation 9, no. 4 (July 12, 2021): SG31—SG46. http://dx.doi.org/10.1190/int-2020-0200.1.
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The extensive development of unconventional reservoirs using horizontal drilling and multistage hydraulic fracturing has generated large volumes of reservoir characterization and production data. The analysis of this abundant data using statistical methods and advanced machine-learning (ML) techniques can provide data-driven insights into well performance. Most predictive modeling studies have focused on the impact that different well completion and stimulation strategies have on well production but have not fully exploited the available in situ rock property data to determine its role in reservoir productivity. We have used machine-learning techniques to rank rock mechanical properties, microseismic attributes, and stimulation parameters in the order of their significance for predicting natural gas production from an unconventional reservoir. The data for this study came from a hydraulically fractured well in the Marcellus Shale in Monongalia County, West Virginia. The data classes included measurements aggregated by well completion stage that included (1) gas production, (2) well-log-derived measurements including bulk density, elastic moduli, shear impedance, compressional impedance, brittleness, and gamma measurements, (3) microseismic attributes, (4) long-period long-duration (LPLD) event counts, (5) fracture counts, and (6) stimulation parameters that included the fluid injection volume and average pumping pressure. To identify observable proxies for the drivers of gas production, we evaluated five commonly used ML approaches including multivariate adaptive regression spline, Gaussian mixture model, random forest, gradient boosting, and neural network. We selected five variables including LPLD event count, seismogenic b-value, hydraulic diffusivity, cumulative moment, and fluid volume as the features most likely to impact gas productivity at the stage level in the study area. The data-driven selection of these parameters for their importance in determining gas production can help reservoir engineers design more effective hydraulic-fracture treatments in the Marcellus Shale and other similar unconventional reservoirs. Plain language summary: We use machine-learning methods and data-driven selection of reservoir parameters to rank and better understand their importance in determining gas production, which can help reservoir engineers design more effective hydraulic-fracture treatments in the Marcellus Shale and other similar unconventional reservoirs.
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Mohamed, Islam A., Adel Othman, and Mohamed Fathy. "A new approach to improve reservoir modeling via machine learning." Leading Edge 39, no. 3 (March 2020): 170–75. http://dx.doi.org/10.1190/tle39030170.1.
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In highly heterogeneous basins with complex subsurface geology, such as the Nile Delta Basin, accurate prediction of reservoir modeling has been a challenge. Reservoir modeling is a continuous process that begins with field discovery and ends with the last phases of production and abandonment. Currently, the stochastic reservoir modeling method is widely used instead of the traditional deterministic modeling method to consider spatial statistics and uncertainties. However, the modeling workflow is demanding and slow, typically requiring months from the initial model concept to flow simulation. In addition, errors from early model stages become cumulative and are difficult to change retroactively. To overcome these limitations, a new workflow is proposed that implements probabilistic neural network inversion to predict reservoir properties. First, well-log data were conditioned properly to match the seismic data scale. Then, the networks were trained and validated, using the conditioned well-log data and seismic internal/external attributes, to predict water saturation and effective porosity 3D volumes. The resulting volumes were sampled in simulation 3D grids and tested using a blind well test. Subsequently, the permeability was calculated from a porosity-permeability relationship inside the reservoir. Finally, a dynamic simulation project of the field was performed in which the historical field production and pressures were compared to the predicted values. One of the Pliocene deepwater turbidite reservoirs in the offshore Nile Delta was used to demonstrate the proposed approach. The results proved the accuracy of the model in predicting the reservoir properties and honoring the heterogeneity of the reservoir. The new approach represents a shortcut for the seismic-to-simulation process, providing a reliable and fast way of constructing a reservoir model and making the seismic-to-simulation process easier.
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Hagen, F., J. G. Gluyas, and G. Goffey. "The Acorn and Beechnut fields, Blocks 29/8a(S), 29/8b, 29/9a(S) and 29/9b, UK North Sea." Geological Society, London, Memoirs 52, no. 1 (2020): 349–59. http://dx.doi.org/10.1144/m52-2018-31.
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AbstractUnocal discovered the Acorn South Field with wells 29/8b-2 and 29/8b-2s in 1983. The well and its side-track found a small accumulation of oil in Upper Jurassic, Fulmar Formation sandstones in an inter-pod setting. Well 29/8b-3 drilled two years later on what was thought to be the same structure found Acorn North, a larger accumulation of oil in a Triassic Skagerrak Formation reservoir on the crest of a Triassic pod. Premier discovered the Beechnut Field two years later, well 29/9b-2 finding oil in the Fulmar and Skagerrak formations in a faulted, inter-pod setting. Both Acorn and Beechnut are deep, high-pressure and high-temperature fields with complex reservoir stratigraphy due to halokinesis during sedimentation and post-depositional structuration. The Skagerrak Formation reservoir in Acorn North is appreciably poorer than similar-age reservoirs further north whilst the Fulmar Formation in Beechnut is relatively poorly developed.Acorn's mid-case oil in place is 90 MMbbl in the Skagerrak Formation and 13 MMbbl in the Fulmar Formation and, for Beechnut, is 15 MMbbl in the Fulmar Formation. Neither field has been developed. Limiting factors include the resource size, variable reservoir development (Beechnut), modest reservoir quality (Acorn North), compartmentalization concerns and development costs.
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El-Behiry, Mohamed G., Said M. Dahroug, and Mohamed Elattar. "Application of geostatistical seismic inversion in reservoir characterization of Sapphire gas field, offshore Nile Delta, Egypt." Leading Edge 38, no. 6 (June 2019): 474–79. http://dx.doi.org/10.1190/tle38060474.1.
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Seismic reservoir characterization becomes challenging when reservoir thickness goes beyond the limits of seismic resolution. Geostatistical inversion techniques are being considered to overcome the resolution limitations of conventional inversion methods and to provide an intuitive understanding of subsurface uncertainty. Geostatistical inversion was applied on a highly compartmentalized area of Sapphire gas field, offshore Nile Delta, Egypt, with the aim of understanding the distribution of thin sands and their impact on reservoir connectivity. The integration of high-resolution well data with seismic partial-angle-stack volumes into geostatistical inversion has resulted in multiple elastic property realizations at the desired resolution. The multitude of inverted elastic properties are analyzed to improve reservoir characterization and reflect the inversion nonuniqueness. These property realizations are then classified into facies probability cubes and ranked based on pay sand volumes to quantify the volumetric uncertainty in static reservoir modeling. Stochastic connectivity analysis was also applied on facies models to assess the possible connected volumes. Sand connectivity analysis showed that the connected pay sand volume derived from the posterior mean of property realizations, which is analogous to deterministic inversion, is much smaller than the volumes generated by any high-frequency realization. This observation supports the role of thin interbed reservoirs in facilitating connectivity between the main sand units.