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1
King, D. E. "Incorporating geological data in well log interpretation." Geological Society, London, Special Publications 48, no. 1 (1990): 45–55. http://dx.doi.org/10.1144/gsl.sp.1990.048.01.06.
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Wu, Xuanzhi, and Edo Nyland. "Automated stratigraphic interpretation of well‐log data." GEOPHYSICS 52, no. 12 (December 1987): 1665–76. http://dx.doi.org/10.1190/1.1442283.
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A computerized well‐log stratigraphic interpretation system based on artificial intelligence can be seen as two steps, contact recognition and interval identification. Unlike previous approaches to mathematical zonation which are essentially statistical, consideration of the geologic environment is included for effective interpretation. Following the logic of human experts, the system first determines the log signature of the contacts. An algorithm incorporating linear regression with variable breakpoints is used to describe the structure of log curves. Log features are mapped to the assigned signature of contacts. Multiple logs are taken into account to set up the final set of contacts, which divides the test borehole into a string of intervals with attributes. A pattern string of intervals is based on the integrated geologic column for the section where the borehole is located. A string‐to‐string matching program then determines the optimal map of the test borehole as the pattern to generate a geologic column based on the computer suggestion. The cost formulas for matching represent knowledge about the stratigraphy of the area under study, and the string‐to‐string matching algorithm includes this knowledge of stratigraphy. In test boreholes in an oil‐sand deposit, 86 percent of the computer identifications of intervals were consistent with those determined by geologists from core descriptions, suggesting that the design concept and computer algorithm are consistent with the characteristics of the interpretation problem.
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Buoro, Alvaro Bueno, and João B. C. Silva. "Ambiguity analysis of well‐log data." GEOPHYSICS 59, no. 3 (March 1994): 336–44. http://dx.doi.org/10.1190/1.1443595.
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Ambiguity in a geophysical interpretation problem is the possibility of accepting more than one solution caused either by solution nonuniqueness or instability. Nonuniqueness is related to the existence of more than one solution regardless of the precision of observations. On the other hand, instability is related to the acceptance of different solutions producing data fittings within the expected observational errors. We studied the ambiguity in the inversion of well‐logging data using a method based on the analysis of a finite number of acceptable solutions, which are ordered, in the solution space, according to their contributions to the overall ambiguity. The analysis of the parameter variations along these ordered solutions provides an objective way to characterize the most ambiguous parameters. Because this analysis is based on the geometry of an ambiguity region, empirically estimated by a finite number of alternative solutions, it is possible to analyze the ambiguity due not only to errors in the observations, but also to discrepancies between the interpretation model and the true geology. Moreover, the analysis can be applied even in the case of a nonlinear interpretation model. The analysis was performed with recorded data, and compared with the analysis using singular value decomposition, leading to comparable results. Following the determination of the most ambiguous parameters, a reparameterization is possible by grouping these parameters into a single parameter leading to a simpler interpretation model and, therefore, to a drastic reduction in the ambiguity.
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Alcolea Rodríguez, Andrés, Paul Marschall, Christophe Nussbaum, and Jens Karl Becker. "Automatic interpretation of geophysical well logs." Geological Society, London, Special Publications 482, no. 1 (September 21, 2018): 25–38. http://dx.doi.org/10.1144/sp482.9.
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AbstractStratigraphic sequences in boreholes are commonly estimated by interpreting combinations of well logs. The interpretation is generally tedious and is made some time after log completion, which often leads to a loss of valuable first-hand information gathered on-site. This may lead to delayed or potentially poor on-site decisions. To make things worse, the standard interpretation of well logs is, at least to a certain degree, subjective and based on the manipulation of data, which may be difficult to trace in the long term. Small changes in lithology are often disregarded and alternating thin layers presenting different lithologies are often combined in one single (notably thicker) stratigraphic unit. Therefore an automatic parameter-based and thus traceable and objective quick look at the lithology immediately after log completion represents both a valuable tool to help with on-site decisions and a solid, mathematically based starting point for further physically based interpretations carried out by log analysts. We present a workflow for the interpretation of well logs defined as an optimization problem. The workflow is applied to the characterization of metre- to decametre-scale stratigraphic units along 13 boreholes in northern Switzerland (one-dimensional resolution) and to millimetre-scale features over a wall at the Mont Terri underground rock laboratory in Switzerland (two-dimensional resolution). The results show that: (1) the workflow accurately maps lithological changes; (2) the interaction with the analyst is minimized, which reduces the subjectivity of the interpretation; and (3) outputs are available for on-site decisions.
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Jahani, Nazanin, Joaquin Ambia Garrido, Sergey Alyaev, Kristian Fossum, Erich Suter, and Carlos Torres-Verdín. "Ensemble-based well-log interpretation and uncertainty quantification for well geosteering." GEOPHYSICS 87, no. 3 (April 11, 2022): IM57—IM66. http://dx.doi.org/10.1190/geo2021-0151.1.
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Hydrocarbon reservoirs are often located in spatially complex and uncertain geologic environments, where the associated costs of drilling wells for exploration and development are notoriously high. These costs may be reduced with an optimized well-placement strategy based on real-time geologic information, known as well geosteering. To effectively place the well in an updated geomodel and support well geosteering decisions in real time, we apply an iterative inversion approach based on the Levenberg-Marquardt form of the ensemble randomized maximum likelihood method. The method estimates geomodel properties together with their uncertainties by reducing the statistical misfit between the measurements acquired with well-logging tools and the predicted measurements from numerical simulations. Analyses of synthetic cases indicate that the method’s reliability and computational speed depend on the distance from the logging tool to formation boundaries, the contrast of model properties, and the thickness of formation layers. Our method delivers reliable estimates of model properties with only 40 ensemble members and 2–10 iterations; hence, it is approximately 10–125 times faster than Metropolis-Hastings Monte Carlo, which we use as a baseline condition given its proven track record. Likewise, our method is amenable to parallelization to further reduce computational times. Implementation of the method with a synthetic example inspired by a historical well geosteering operation yields accurate formation evaluation and verifies its accurate and reliable performance under complex geologic conditions.
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Spalburg, Mirano R. "A Fast and Transparent Bayesian Log Interpretation Method." Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description 63, no. 4 (August 1, 2022): 534–48. http://dx.doi.org/10.30632/pjv63n4-2022a4.
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This paper presents a fast and transparent Bayesian-based computational method that can be used for the interpretation of well logs. Rather than single values for formation properties, the results are probability distributions. The method is fast because the responses of logging tools, for a large set of formation realizations, are calculated only once and then stored with the realizations in a database. Thereafter, only this database is used for the interpretation of real well logs. The method is transparent because it entirely relies on selecting formation realizations with calculated log responses that are, within a given error margin, equal to the real logging tool responses. Therefore, only internally consistent interpretations can be found. The size of the database is realizable and the log evaluation computation time sufficiently short. The method has been tested with good results on more than 100 wells. Some of the well results are presented and discussed. The presented evaluations use databases of about 300 MB containing about 25 million realizations. Database construction and whole well evaluation each require less than a few seconds.
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Sari, Tri Wulan, and Sujito Sujito. "LITHOLOGY INTERPRETATION BASED ON WELL LOG DATA ANALYSIS IN “JS” FIELD." Applied Research on Civil Engineering and Environment (ARCEE) 1, no. 01 (October 28, 2019): 31–37. http://dx.doi.org/10.32722/arcee.v1i01.1955.
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Reservoir lithology types in a prospect zone of hydrocarbon can be known through well log data analysis, both qualitatively and quantitatively. Lithology interpretation based on qualitatively well log data analysis, has been successfully carried out by K-1 and K-3 well log data on JS Field, West Natuna basin, Riau Islands.Main focus of the research is types of lithology indicated by response the petrophysical well data log of Lower-Middle Miocene Arang Formation. Arang Formation was deposited immediately on top Barat formation and depositional environment in this formation is transitional marine - marine. Petrophysics log shows well data are log gamma ray, resistivity, neutron porosity, density, and sonic. The limitation of study are on four types lithology, they are coal, sand, sally sand, and shale. Lithology on well K-1 dominate by shale, there is thin intersection between sand and coal. The well of K-1 have sand thickest around six meter. While on well K-3 Petrophysics log data shows thin intersection between coal, sand, shaly sand, and dominated by shale. The thickest Sand have thickness 29 meter, and thicker than on K-1 well. The result in this study, the formation dominated by types of lithology “shale”.
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ASAKURA, NATSUO. "3D seismic interpretation integrated by VSP and well log data." Journal of the Japanese Association for Petroleum Technology 51, no. 1 (1986): 2–15. http://dx.doi.org/10.3720/japt.51.2.
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Szucs, Peter, and Faruk Civan. "Multi-layer well log interpretation using the simulated annealing method." Journal of Petroleum Science and Engineering 14, no. 3-4 (May 1996): 209–20. http://dx.doi.org/10.1016/0920-4105(95)00048-8.
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Ezeh, Sunny C., Wilfred A. Mode, Berti M. Ozumba, and Nura A. Yelwa. "Sedimentology and ichnology of Neogene Coastal Swamp deposits in the Niger Delta Basin, Nigeria." Geologos 22, no. 3 (September 1, 2016): 191–200. http://dx.doi.org/10.1515/logos-2016-0020.
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Abstract Often analyses of depositional environments from sparse data result in poor interpretation, especially in multipartite depositional settings such as the Niger Delta. For instance, differentiating channel sandstones, heteroliths and mudstones within proximal environments from those of distal facies is difficult if interpretations rely solely on well log signatures. Therefore, in order to achieve an effective and efficient interpretation of the depositional conditions of a given unit, integrated tools must be applied such as matching core descriptions with wireline log signature. In the present paper cores of three wells from the Coastal Swamp depositional belt of the Niger Delta are examined in order to achieve full understanding of the depositional environments. The well sections comprise cross-bedded sandstones, heteroliths (coastal and lower shoreface) and mudstones that were laid down in wave, river and tidal processes. Interpretations were made from each data set comprising gamma ray logs, described sedimentological cores showing sedimentary features and ichnological characteristics; these were integrated to define the depositional settings. Some portions from one of the well sections reveal a blocky gamma ray well log signature instead of a coarsening-upward trend that characterises a shoreface setting while in other wells the signatures for heteroliths at some sections are bell blocky in shaped rather than serrated. Besides, heteroliths and mudstones within the proximal facies and those of distal facies were difficult to distinguish solely on well log signatures. However, interpretation based on sedimentology and ichnology of cores from these facies was used to correct these inconsistencies. It follows that depositional environment interpretation (especially in multifarious depositional environments such as the Niger Delta) should ideally be made together with other raw data for accuracy and those based solely on well log signatures should be treated with caution.
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Al-Ateya, Wisam H., Amna M. Handhal, and Hasan A. Hashem. "Well Log Interpretation and Petrophysical Modeling to Evaluate Zubair Reservoir in North-Rumaila Oil Field." Iraqi Geological Journal 55, no. 2A (July 31, 2022): 208–19. http://dx.doi.org/10.46717/igj.55.2a.15ms-2022-07-31.
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This study uses well log interpretation and petrophysical modeling, which is one of the most influential tools for the evaluation of Zubair Reservoir in the X Oil Field. Understanding reservoir parameters and boosting output by picking the optimal location for future drilling candidates will be helped by these interpretations and modeling. 3D seismic and well log data were used to create and describe a petrophysical model in this investigation. Each parameter was upscaled when interpreting well logs and calculating petrophysical characteristics. The petrophysical model was created using geostatistical approaches such as Gaussian simulation, variogram, and Monte Carlo simulation. Techlog, Petrel, and HRS Software were used in this study. As a result, we analyzed porosity, shale volume, and water saturation for four wells named A, B, C, and C using a set of commercial software and applying the different well logs like; gamma-ray (gr) log, spontaneous potential log, porosity logs (Density Log, Neutron Log, and Sonic Log), and resistivity log. The range of porosity values is 0.23 to 0.30, the water saturation is around 10% in the crest and 60% in the flank, and Vsh is interbedded with sand and the value is decreasing 25% in the clean sand bed. This formation is characterized by clean sand and is interbedded with a sharp boundary. The sand in the main pay is characterized by homogeneous and uniform porosity patterns. The homogeneous nature of the sand leads to uniform saturation of water to fill this entire sand unit with oil and the absence of a transition zone.
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Stroyanetskaya, G. E., and E. A. Malykh. "WELL LOG DATA INTERPRETATION PROBLEMS IN WELL, DRILLED USING POLYMER-CLAY-BASED DRILLING MUD." Oil and Gas Studies, no. 5 (November 1, 2017): 47–53. http://dx.doi.org/10.31660/0445-0108-2017-5-47-53.
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There was shown the low efficiency of standard well logging sequence for reservoirs identification and de-fining their saturation in the well, penetrating on polymer-clay-based drilling mud the section with high salinity of formation water. At low resistances of formations the reliable results of testing as for large volume reservoirs as well as for small volume reservoirs are needed for establishing the critical values of indications of induction method and electrical measurements against the permeable formations of different thickness. To distinguish the reservoirs it is necessary to obtain the core samples and to analyze them to establish the compliance of indications between geophysical measurements and reservoir characteristics.
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Sakuyama, Naofumi. "Permeability interpretation of well log data for different types of reservoirs." Journal of the Japanese Association for Petroleum Technology 77, no. 1 (2012): 42–49. http://dx.doi.org/10.3720/japt.77.42.
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Khandelwal, M., and T. N. Singh. "Artificial Neural Networks as a Valuable Tool for Well Log Interpretation." Petroleum Science and Technology 28, no. 14 (August 2, 2010): 1381–93. http://dx.doi.org/10.1080/10916460903030482.
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Escobar, Freddy Humberto, Carlos Andrés Torregrosa Marlés, and Guiber Olaya Marín. "Interference test interpretation in naturally fractured reservoirs." DYNA 87, no. 214 (July 1, 2020): 121–28. http://dx.doi.org/10.15446/dyna.v87n214.82733.
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The naturally fractured reservoir characterization is crucial because it can help to predict the flow pattern of fluids, and the storativity ratio of the fractures and to understand whether two or more wells have communication, among others. This paper presents a practical methodology for interpreting interference tests in naturally fractured reservoirs using characteristic points found on the pressure derivative curve. These kinds of tests describe a system that consists of a producing well and an observation well separated by a distance (r). Using characteristic points and features found on the pressure and pressure derivative log-log plot, Analytical expressions were developed from the characteristic points of the pressure and pressure derivative log-log plot to determine the interporosity flow parameter (λ) and the storativity ratio of the fractures (ω). Finally, examples are used to successfully verify the expressions developed so that the naturally-fractured parameters were reproduced with good accuracy.
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Jacobson, Larry, Venkataraman Jambunathan, Zhipeng Liu, and Weijun Guo. "Technical advances in pulsed-neutron interpretation for cased-hole logging: Physics, interpretation, and log examples." Interpretation 3, no. 1 (February 1, 2015): SA159—SA166. http://dx.doi.org/10.1190/int-2014-0174.1.
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Recently developed multidetector pulsed-neutron tools (MDPNTs — a term describing a pulsed-neutron tool with at least three detectors) can provide three-phase formation fluid analysis in cased wells. These tools are 43 mm (1 11/16 in.) or 54 mm (2 1/8 in.) in diameter and can be logged in or below most tubing sizes. We reviewed traditional oil- and water-saturation techniques as well as indirect gas-saturation techniques, and we compared them with recently developed direct gas-saturation techniques, now available from MDPNTs. A log example developed the data verification and interpretation process. The interpretation process was divided into two parts: First, we verified the log data quality and second, we applied a newly developed gas model to the log data providing gas saturation without any reliance on the previously determined oil and water saturation.
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Viggen, Erlend Magnus, Lasse Løvstakken, Svein-Erik Måsøy, and Ioan Alexandru Merciu. "Better Automatic Interpretation of Cement Evaluation Logs through Feature Engineering." SPE Journal 26, no. 05 (February 23, 2021): 2894–913. http://dx.doi.org/10.2118/204057-pa.
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Summary We investigate systems to automatically interpret cement evaluation logs using supervised machine learning (ML). Such systems can provide instant rough interpretations that may then be used as a basis for human interpretation. Here, we compare the performance of two approaches, one previously published and one new. The previous approach is based on deep convolutional neural networks (CNNs) that autonomously learn to extract features from well log data, whereas the new approach uses feature engineering, in which we use our own domain knowledge to extract features. We base this work on a data set of approximately 60 km of well log data. Specialist interpreters have classified these logs according to the bond quality (BQ; six ordinal classes) and hydraulic isolation (HI; two classes) of solids outside the casing. We train the ML systems to reproduce these reference interpretations in segments of 1 m in length. The CNNs directly receive log data as a collection of 2D images and 1D curves. In the feature-engineering approach, we combine the extracted features with various classifiers. For BQ, the CNNs' interpretation exactly matches the reference 51.6% of the time. It does not miss by more than one class 88.5% of the time. For HI, the CNNs match the reference 86.7% of the time. The best-performing feature-based classifier, which is an ensemble of individual classifiers, provides better results of 57.4, 89.5, and 88.9%, respectively. Our results indicate two main reasons why feature-based classifiers may perform particularly well on this task. First, there is some subjectivity inherent in the well log interpretations that are used to train and test ML systems. Second, well logs comprise many different and complex pieces of data. For these reasons, this data set may be particularly liable to overfitting. This may favor approaches based on feature engineering, where we apply our domain knowledge to extract a few pieces of essential information from the data instead of leaving the job of understanding the data to an ML system that may misinterpret spurious patterns as generalizable. It may also favor simpler classifiers with less overfitting capacity. This paper shows how petroleum researchers and engineers can implement automatic interpretation systems for cement evaluation logs using ML methods that are easier to apply and deploy while also performing better than an approach based on autonomous feature extraction. This approach could also be adapted for automatic interpretation of other types of well log data.
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Ogaya, Xènia, Juan Alcalde, Ignacio Marzán, Juanjo Ledo, Pilar Queralt, Alex Marcuello, David Martí, Eduard Saura, Ramon Carbonell, and Beatriz Benjumea. "Joint interpretation of magnetotelluric, seismic, and well-log data in Hontomín (Spain)." Solid Earth 7, no. 3 (June 9, 2016): 943–58. http://dx.doi.org/10.5194/se-7-943-2016.
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Abstract. Hontomín (N of Spain) hosts the first Spanish CO2 storage pilot plant. The subsurface characterization of the site included the acquisition of a 3-D seismic reflection and a circumscribed 3-D magnetotelluric (MT) survey. This paper addresses the combination of the seismic and MT results, together with the available well-log data, in order to achieve a better characterization of the Hontomín subsurface. We compare the structural model obtained from the interpretation of the seismic data with the geoelectrical model resulting from the MT data. The models correlate well in the surroundings of the CO2 injection area with the major structural differences observed related to the presence of faults. The combination of the two methods allowed a more detailed characterization of the faults, defining their geometry, and fluid flow characteristics, which are key for the risk assessment of the storage site. Moreover, we use the well-log data of the existing wells to derive resistivity–velocity relationships for the subsurface and compute a 3-D velocity model of the site using the 3-D resistivity model as a reference. The derived velocity model is compared to both the predicted and logged velocity in the injection and monitoring wells, for an overall assessment of the computed resistivity–velocity relationships. The major differences observed are explained by the different resolution of the compared geophysical methods. Finally, the derived velocity model for the near surface is compared with the velocity model used for the static corrections in the seismic data. The results allowed extracting information about the characteristics of the shallow unconsolidated sediments, suggesting possible clay and water content variations. The good correlation of the velocity models derived from the resistivity–velocity relationships and the well-log data demonstrate the potential of the combination of the two methods for characterizing the subsurface, in terms of its physical properties (velocity, resistivity) and structural/reservoir characteristics. This work explores the compatibility of the seismic and magnetotelluric methods across scales highlighting the importance of joint interpretation in near surface and reservoir characterization.
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Owen, Thomas E. "Fundamentals of well-log interpretation. Part 1: The acquisition of logging data." Journal of Petroleum Science and Engineering 4, no. 2 (May 1990): 183–86. http://dx.doi.org/10.1016/0920-4105(90)90025-x.
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Wolpert, Philipp J., and Michael C. Poppelreiter. "Borehole-image-log characterization of deltaic deposits from a behind-outcrop well: Opportunities and limitations." Journal of Sedimentary Research 89, no. 12 (December 17, 2019): 1207–30. http://dx.doi.org/10.2110/jsr.2019.59.
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ABSTRACT Borehole imaging (BHI) is a fast and precise method for collecting subsurface data. Rock calibration may reduce uncertainties inherent in interpretations of BHI logs. However, only few data sets are published that link borehole image facies to core and outcrop facies of deltaic successions. The objective of this paper is to compare and contrast sedimentologic features as seen in BHI, core, and outcrop, using a structured and hierarchical workflow. The research will provide a global framework for the interpretation of borehole images in similar environments. A shallow, behind outcrop, research well (“Mondot-1”) drilled in NW Spain, penetrated a 185.50 m (TVD) thick section of the deltaic Sobrarbe Formation. A Formation MicroImager (FMI) borehole image log and a comprehensive well log suite was acquired in the fully cored well. The Eocene Sobrarbe Formation consists mostly of siliciclastic and some carbonate facies. Rapid vertical and lateral facies changes over a few tens of meters are observed in outcrops of the Sobrarbe Formation. The cored part of the formation is composed of argillaceous sandstone and carbonate with few diagnostic sedimentary features that can be used to constrain a conceptual depositional model. To provide a sedimentologically sound interpretation of this FMI log, we focused on layers showing diagnostic sedimentary features. Subsequently, facies associations and stratigraphic sequences were interpreted. Each facies association contained sedimentologic tie points that anchored the interpretation with diagnostic features such as slumps. This paper suggests interpreting BHI false-color images of deltaic successions using conceptual geologic constraints such as 1) depositional tie points, 2) genetically related facies associations, and 3) a hierarchical stratigraphic framework, to establish meaningful conceptual depositional models.
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Wolpert, Philipp J., and Michael C. Poppelreiter. "Borehole-image-log characterization of deltaic deposits from a behind-outcrop well: Opportunities and limitations." Journal of Sedimentary Research 89, no. 12 (December 17, 2019): 1207–30. http://dx.doi.org/10.2110/jsr.2019.75.
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ABSTRACT Borehole imaging (BHI) is a fast and precise method for collecting subsurface data. Rock calibration may reduce uncertainties inherent in interpretations of BHI logs. However, only few data sets are published that link borehole image facies to core and outcrop facies of deltaic successions. The objective of this paper is to compare and contrast sedimentologic features as seen in BHI, core, and outcrop, using a structured and hierarchical workflow. The research will provide a global framework for the interpretation of borehole images in similar environments. A shallow, behind outcrop, research well (“Mondot-1”) drilled in NW Spain, penetrated a 185.50 m (TVD) thick section of the deltaic Sobrarbe Formation. A Formation MicroImager (FMI) borehole image log and a comprehensive well log suite was acquired in the fully cored well. The Eocene Sobrarbe Formation consists mostly of siliciclastic and some carbonate facies. Rapid vertical and lateral facies changes over a few tens of meters are observed in outcrops of the Sobrarbe Formation. The cored part of the formation is composed of argillaceous sandstone and carbonate with few diagnostic sedimentary features that can be used to constrain a conceptual depositional model. To provide a sedimentologically sound interpretation of this FMI log, we focused on layers showing diagnostic sedimentary features. Subsequently, facies associations and stratigraphic sequences were interpreted. Each facies association contained sedimentologic tie points that anchored the interpretation with diagnostic features such as slumps. This paper suggests interpreting BHI false-color images of deltaic successions using conceptual geologic constraints such as 1) depositional tie points, 2) genetically related facies associations, and 3) a hierarchical stratigraphic framework, to establish meaningful conceptual depositional models.
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Baker, P. L. "Log analysis systems incorporating artificial intelligence techniques ? some examples." Exploration Geophysics 20, no. 2 (1989): 331. http://dx.doi.org/10.1071/eg989331.
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Artificial Intelligence (Al) systems have been used with some success in the areas of dipmeter interpretation, quantitative log interpretation and well-to-well correlation. A prototype expert system has been developed using a rule-based approach to lithology identification. Extensions of the system are now being considered to do mineral identification for the problem of mineral model construction for multi-mineral log interpretation algorithms.
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Zhang, Jia, Shiqing Cheng, Shiying Di, Zhanwu Gao, Rui Yang, and Pengliang Cao. "A Two-Phase Numerical Model of Well Test Analysis to Characterize Formation Damage in Near-Well Regions of Injection Wells." Geofluids 2021 (February 18, 2021): 1–15. http://dx.doi.org/10.1155/2021/6693965.
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Formation damage usually occurs in near-well regions for injection wells completed in offshore oilfields under the development of line drive patterns. However, current works on characterizing the damage by well test analysis were basically focused on using single-phase analogy to solve two-phase flow issues, resulting in errors on the diagnosis and interpretation of transient pressure data. In this paper, we developed a two-phase model to simulate the pressure transient behavior of a water injection well in a multiwell system. To solve the model more efficiently, we used the finite volume method to discretize partially differential flow equations in a hybrid grid system, including both Cartesian and radial meshes. The fully implicit Newton-Raphson method was also employed to solve the equations in our model. With this methodology, we compared the resulting solutions with a commercial simulator. Our results keep a good agreement with the solutions from the simulator. We then graphed the solutions on a log-log plot and concluded that the effects of transitional zone and interwell interference can be individually identified by analyzing specific flow regimes on the plot. Further, seven scenarios were raised to understand the parameters which dominate the pressure transient behavior of these flow regimes. Finally, we showed a workflow and verified the applicability of our model by demonstrating a case study in a Chinese offshore oilfield. Our model provides a useful tool to reduce errors in the interpretation of pressure transient data derived from injection wells located in a line drive pattern.
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Wang, Lingqian, Hui Zhou, and Hanming Chen. "Adaptive Feature Map-Guided Well-Log Interpolation." Remote Sensing 15, no. 2 (January 12, 2023): 459. http://dx.doi.org/10.3390/rs15020459.
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As an irreplaceable quantitative interpretation method, prestack seismic inversion enables the effective estimation of subsurface elastic parameters for reservoir prediction. However, for the model-driven prestack seismic inversion, the band-limited characteristics and noise interference of observed seismic data result in its high dependence on the initial models. This suggests that reasonable initial models act as a supplement to reliable variation trends in formation and can reduce the non-uniqueness of inversion results. In this article, we introduce a well-log interpolation method with a feature map-guided non-local means algorithm, which is for establishing high-fidelity initial models used for prestack seismic inversion. First, we briefly review the basic theory of general model-driven prestack seismic inversion. Then, we use dictionary learning to split the poststack seismic record into patches, and represent them with sparse vectors, instead of directly using seismic record. The advantage of dictionary learning is that it can adaptively extract useful signals from noisy observed data and provide fine structures by sparse reconstruction. Therefore, the proposed feature extraction method can improve the noise immunity and reliability of the well-log interpolation. More accurate initial models are pre-constructed efficiently by our feature extraction method, which improves the reliability of prestack seismic inversion results. Two kinds of observed seismic data are used, including the poststack seismic record for well-log interpolation and prestack seismic data used for inversion. Synthetic and field data tests both demonstrate the favorable performance of the proposed well-log interpolation method. In summary, a novel and convenient initial model building approach is provided, which contributes to seismic exploration and geologic modeling.
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Zughar, Sarah S., Ahmad A. Ramadhan, and Ahmed K. Jaber. "Petrophysical Properties of an Iraqi Carbonate Reservoir Using Well Log Evaluation." Iraqi Journal of Chemical and Petroleum Engineering 21, no. 1 (March 30, 2020): 53–59. http://dx.doi.org/10.31699/ijcpe.2020.1.8.
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This research was aimed to determine the petrophysical properties (porosity, permeability and fluid saturation) of a reservoir. Petrophysical properties of the Shuiaba Formation at Y field are determined from the interpretation of open hole log data of six wells. Depending on these properties, it is possible to divide the Shuiaba Formation which has thickness of a proximately 180-195m, into three lithological units: A is upper unit (thickness about 8 to 15 m) involving of moderately dolomitized limestones; B is a middle unit (thickness about 52 to 56 m) which is composed of dolomitic limestone, and C is lower unit ( >110 m thick) which consists of shale-rich and dolomitic limestones. The results showed that the average formation water resistivity for the formation (Rw = 0.021), the average resistivity of the mud filtration (Rmf = 0.57), and the Archie parameters determined by the picket plot method, where m value equal to 1.94, n value equal to 2 and a value equal to 1. Porosity values and water saturation Sw were calculated along with the depth of the composition using IP V3.5 software. The interpretation of the computer process (CPI) showed that the better porous zone holds the highest amount of hydrocarbons in the second zone. From the flow zone indicator method, there are four rock types in the studied reservoir.
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Hardage, Bob A. "Pitfall experiences when interpreting complex structure with low-quality seismic images." Interpretation 3, no. 1 (February 1, 2015): SB29—SB37. http://dx.doi.org/10.1190/int-2014-0118.1.
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Structural interpretation of seismic data presents numerous opportunities for encountering interpretational pitfalls, particularly when a seismic image does not have an appropriate signal-to-noise ratio (S/N), or when a subsurface structure is unexpectedly complex. When both conditions exist — low S/N data and severe structural deformation — interpretation pitfalls are almost guaranteed. We analyzed an interpretation done 20 years ago that had to deal with poor seismic data quality and extreme distortion of strata. The lessons learned still apply today. Two things helped the interpretation team develop a viable structural model of the prospect. First, existing industry-accepted formation tops assigned to regional wells were rejected and new log interpretations were done to detect evidence of repeated sections and overturned strata. Second, the frequency content of the 3D seismic data volume was restricted to only the first octave of its seismic spectrum to create better evidence of fault geometries. A logical and workable structural interpretation resulted when these two action steps were taken. To the knowledge of our interpretation team, neither of these approaches had been attempted in the area at the time of this work (early 1990s). We found two pitfalls that may be encountered by other interpreters. The first pitfall was the hazard of accepting long-standing, industry-accepted definitions of the positions of formation tops on well logs. This nonquestioning acceptance of certain log signatures as indications of targeted formation tops led to a serious misinterpretation in our study. The second pitfall was the prevailing passion by geophysicists to create seismic data volumes that have the widest possible frequency spectrum. This interpretation effort showed that the opposite strategy was better at this site and for our data conditions; i.e., it was better to filter seismic images so that they contained only the lowest octave of frequencies in the seismic spectrum.
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Einstein, Elizabeth E., and Ken W. Edwards. "Comparison of an Expert System to Human Experts in Well-Log Analysis and Interpretation." SPE Formation Evaluation 5, no. 01 (March 1, 1990): 39–45. http://dx.doi.org/10.2118/18129-pa.
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Szabó, N. P., and M. Dobróka. "Interval inversion as innovative well log interpretation tool for evaluating organic-rich shale formations." Journal of Petroleum Science and Engineering 186 (March 2020): 106696. http://dx.doi.org/10.1016/j.petrol.2019.106696.
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Morozov, Igor B., and Jinfeng Ma. "Accurate poststack acoustic-impedance inversion by well-log calibration." GEOPHYSICS 74, no. 5 (September 2009): R59—R67. http://dx.doi.org/10.1190/1.3170687.
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The seismic-impedance inversion problem is underconstrained inherently and does not allow the use of rigorous joint inversion. In the absence of a true inverse, a reliable solution free from subjective parameters can be obtained by defining a set of physical constraints that should be satisfied by the resulting images. A method for constructing synthetic logs is proposed that explicitly and accurately satisfies (1) the convolutional equation, (2) time-depth constraints of the seismic data, (3) a background low-frequency model from logs or seismic/geologic interpretation, and (4) spectral amplitudes and geostatistical information from spatially interpolated well logs. The resulting synthetic log sections or volumes are interpretable in standard ways. Unlike broadly used joint-inversion algorithms, the method contains no subjectively selected user parameters, utilizes the log data more completely, and assesses intermediate results. The procedure is simple and tolerant to noise, and it leads to higher-resolution images. Separating the seismic and subseismic frequency bands also simplifies data processing for acoustic-impedance (AI) inversion. For example, zero-phase deconvolution and true-amplitude processing of seismic data are not required and are included automatically in this method. The approach is applicable to 2D and 3D data sets and to multiple pre- and poststack seismic attributes. It has been tested on inversions for AI and true-amplitude reflectivity using 2D synthetic and real-data examples.
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Almamy, Jehan A. "Extended Poisson-Log-Logistic Distribution." International Journal of Statistics and Probability 8, no. 2 (January 15, 2019): 56. http://dx.doi.org/10.5539/ijsp.v8n2p56.
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In this work, we introduce a new Poisson-log-logistic distribution with a physical interpretation and some applications. Some essential properties are derived. Modeling of four real data sets are provided to illustrate the wide applicability of the new model in differnt fields like finance, reliability, economy and medicine. The new compound model is better than other well-known competitive models which have at least the same number of parameters.
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Hodlur, G. K., Ratnakar Dhakate, and Rolland Andrade. "Correlation of vertical electrical sounding and borehole-log data for delineation of saltwater and freshwater aquifers." GEOPHYSICS 71, no. 1 (January 2006): G11—G20. http://dx.doi.org/10.1190/1.2169847.
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In a case history from the Mahanadi basin (India), we demonstrate the use of resistivity data from electric-log soundings and from borehole logs to discriminate between saltwater and freshwater aquifers. We use interpreted data from eight surface-based vertical electrical soundings (VES) and electric well logs from three boreholes in this study. We establish a quantitative relation among longitudinal unit conductance S (obtained from VES), water resistivity [Formula: see text], and layer thickness h. We show that ambiguities in resistivity data interpretation limit its ability to distinguish between freshwater and saltwater aquifers. Electric well-log data interpretation is much more accurate but requires boreholes, which are not cost effective when exploring for groundwater. Integrating well-log-based estimates of [Formula: see text] into resistivity interpretation of surface-based soundings improves its ability to discriminate freshwater aquifers while maintaining cost-effective exploration.
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Xue, Zong An, and Yi Ping Wu. "Well-Log Evaluation of Vuggy Carbonate Reservoir in Middle East." Applied Mechanics and Materials 651-653 (September 2014): 1302–5. http://dx.doi.org/10.4028/www.scientific.net/amm.651-653.1302.
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The typical characteristics of carbonate reservoir is heterogeneous. The reservoirs were deposited in slope of marginal neritic carbonate plat form and marginal reefs in Middle East Oil Field. The vuggy carbonate reservoir pore systems include intergranular pores, mould pores, intercrystal pores, micropores and dissolution fracture. I t can be divided into separate vugs and touching vugs on the basis of vug interconnection. The goal of well-log evaluation is to describe the spatial distribution of petrophysical parameters, such as porosity and permeability. Well-log evaluation and core analyses provide quantitative measurements of petrophysical parameters in the vicinity of the well bore. The key for quantifying physics models is buildup the relationship between the log data and the core analyses result. The purpose of reservoir evaluation is to use the Interactive Mineral Solver module of Interactive Physics software to solve for mineralogy, porosity and permeability. The result of the analyses shows that calculated parameters has high coherence with core sample test. For vuggy carbonate reservoir evaluation, It shows that accurate values of physics parameters can be predicted using selected module in well-log data processing and interpretation.
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Hossain, Touhid Mohammad, Junzo Watada, Izzatdin A. Aziz, and Maman Hermana. "Machine Learning in Electrofacies Classification and Subsurface Lithology Interpretation: A Rough Set Theory Approach." Applied Sciences 10, no. 17 (August 27, 2020): 5940. http://dx.doi.org/10.3390/app10175940.
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Initially, electrofacies were introduced to define a set of recorded well log responses in order to characterize and distinguish a bed from the other rock units, as an advancement to the conventional application of well logs. Well logs are continuous records of several physical properties of drilled rocks that can be related to different lithologies by experienced log analysts. This work is time consuming and likely to be imperfect because human analysis is subjective. Thus, any automated classification approach with high promptness and accuracy is very welcome by log analysts. One of the crucial requirements in petroleum engineering is to interpret a bed’s lithology, which can be done by grouping a formation into electrofacies. In the past, geophysical modelling, petro-physical analysis, artificial intelligence and several statistical method approaches have been implemented to interpret lithology. In this research, important well log features are selected by using the Extra Tree Classifier (ETC), and then five individual electrofacies are constructed by using the selected well log features. Finally, a rough set theory (RST)-based whitebox classification approach is proposed to classify the electrofacies by generating decision rules. These rules are later on used to determine the lithology classes and we found that RST is beneficial for performing data mining tasks such as data classification and rule extraction from uncertain and vague well log datasets. A comparison study is also provided, where we use support vector machine (SVM), deep learning based on feedforward multilayer perceptron (MLP) and random forest classifier (RFC) to compare the electrofacies classification accuracy.
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Kamilah, Tsara, Maman Hermana, Luluan Almanna, Siti Sorhana Syazwani Mokhtar, and Raghel Yunginger. "Depositional environment characterization in deltaic system by utilizing core, well log and seismic attribute data, Northern Malay Basin." IOP Conference Series: Earth and Environmental Science 1003, no. 1 (April 1, 2022): 012013. http://dx.doi.org/10.1088/1755-1315/1003/1/012013.
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Abstract The heterogeneity of shallow marine facies is quite hard to be solved. However, integrating all the data is essential to determine the details of facies distribution, whereas facies characterization is an important part that should be done before the petrophysical characterisation. This study aims to evaluate the reservoir depositional facies distribution using seismic well log and core data based on sequence stratigraphy concept, seismic interpretation, and seismic attributes, thus reducing the risk of oil exploration and improving the success rate. To ensure the objective is achieved, sequence stratigraphy interpretation has been done using integrating well log, core, and seismic data to identify the depositional environment. Distribution of coal helps to create well log correlation. Lithofacies and fluid have been interpreted according to core, test and log patterns, such as; gas sand, brine sand, shale, and coal. Based on the characteristic of logs, biostratigraphy and core data, five depositional environments have been interpreted in this study including in-distributary channel, delta plain (flood plain shale), crevasse splay, delta front shale (prodelta), and mouth bar. The interval of the target area is generally influenced by regression systems where on the lower formation is dominated by the transition depositional environment and changed to be dominant of delta plain.
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Sakhnyuk, Vladlen I., Evgeniy V. Novikov, Alexander M. Sharifullin, Vasiliy S. Belokhin, Alexey P. Antonov, Mikhail U. Karpushin, Maria A. Bolshakova, Sergey A. Afonin, Roman S. Sautkin, and Anna A. Suslova. "Machine learning applications for well-logging interpretation of the Vikulov Formation." Georesursy 24, no. 2 (May 16, 2022): 230–38. http://dx.doi.org/10.18599/grs.2022.2.21.
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Nowadays well logging curves are interpreted by geologists who preprocess the data and normalize the curves for this purpose. The preparation process can take a long time, especially when hundreds and thousands of wells are involved. This paper explores the applicability of Machine Learning methods to geology tasks, in particular the problem of lithology interpretation using well-logs, and also reveals the issue of the quality of such predictions in comparison with the interpretation of specialists. The authors of the article deployed three groups of Machine Learning algorithms: Random Forests, Gradient Boosting and Neural Networks, and also developed its own metric that takes into account the geological features of the study area and statistical proximity of lithotypes based on log curves values.
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Skibitskaya, N. A., V. A. Kuzmin, M. N. Bolshakov, K. V. Kovalenko, E. A. Babich, N. I. Samokhvalov, and O. O. Marutyan. "Methodology for determining the average diameter of rock pores and channels by well-log data based on lithological and petrophysical studies." Actual Problems of Oil and Gas, no. 32 (April 21, 2021): 47–57. http://dx.doi.org/10.29222/ipng.2078-5712.2021-32.art4.
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The paper presents a method for determining the parameters characterizing the features of the pore space structure of rocks continuously along the section, based on the results of well-log data interpretation. The results of the well-log data interpretation are presented with diagrams of average values of pore and channel diameters calculated continuously along the section, which are consistent with the results of laboratory measurements on rock samples. The proposed method is based on the use of the established correlation relationships between the slope tangent of the graph of dependence of gas permeability on reciprocal of mean pressure drop and the parameters of the pore space structure, determined from the digital processing of raster electron microscopic images.
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Zhang, Qiong, and Jean-Baptist Peyaud. "Development and Validation of a Novel Interpretation Algorithm for Enhanced Resolution of Well Logging Signals." Journal of Sensors 2021 (January 7, 2021): 1–10. http://dx.doi.org/10.1155/2021/6610806.
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This work presents a novel algorithm that achieves enhanced resolution of well logging signals, e.g., from 1 ft of a pulsed neutron mineralogy tool to 0.04 ft of an imaging tool. The algorithm, denoted as “Digital Core,” combines mineralogical and sedimentological information to generate a high-resolution record of the formation mineralogy which can be consequently applied to thin bedded environments. The keystone to the philosophy of this algorithm is that the spectral information recorded by mineralogy tool is a weighted average of the mineralogy of each lithological component in the analyzed volume. Therefore, by using a high-resolution image log to determine the proportion of each lithological component, their composition can be determined from the mineralogy log data. A field case from a well located in South Australia is presented in this work, and the results validate the feasibility of an integrated core-level petrophysical analysis in a cost-effective and timely manner compared to conventional core measurements.
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Zhang, Qiong, and Jean-Baptist Peyaud. "Development and Validation of a Novel Interpretation Algorithm for Enhanced Resolution of Well Logging Signals." Journal of Sensors 2021 (January 7, 2021): 1–10. http://dx.doi.org/10.1155/2021/6610806.
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This work presents a novel algorithm that achieves enhanced resolution of well logging signals, e.g., from 1 ft of a pulsed neutron mineralogy tool to 0.04 ft of an imaging tool. The algorithm, denoted as “Digital Core,” combines mineralogical and sedimentological information to generate a high-resolution record of the formation mineralogy which can be consequently applied to thin bedded environments. The keystone to the philosophy of this algorithm is that the spectral information recorded by mineralogy tool is a weighted average of the mineralogy of each lithological component in the analyzed volume. Therefore, by using a high-resolution image log to determine the proportion of each lithological component, their composition can be determined from the mineralogy log data. A field case from a well located in South Australia is presented in this work, and the results validate the feasibility of an integrated core-level petrophysical analysis in a cost-effective and timely manner compared to conventional core measurements.
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Niculescu, Bogdan Mihai, and Victor Mocanu. "Characterization of Pliocene Biogenic Gas Reservoirs from the Western Black Sea Shelf (Romanian Offshore) by Integration of Well Logs and Core Data." Energies 14, no. 20 (October 14, 2021): 6629. http://dx.doi.org/10.3390/en14206629.
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The successful interpretation of open-hole well logging data relies on jointly using all available petrophysical and geological information. This paper presents relevant case studies related to the integration of well logs with core measurements for exploration wells drilled in the Romanian continental shelf area of the Western Black Sea basin. The analyzed wells targeted gas-bearing sands and silts complexes of Early Pliocene (Dacian) age, developed in a deltaic to shallow marine sedimentary environment in two distinct fields. The wireline logging programs included conventional formation evaluation logs, pressure surveys, nuclear magnetic resonance, and borehole electrical imaging logs. The core dataset comprised routine and special measurements (porosity, grain density, permeability, water saturation, and Archie parameters) carried out at quasi-reservoir confining pressure. The wireline logging suites were interpreted via a deterministic workflow, including core-derived interpretation parameters. Other core-derived parameters were used for constraining and validating the log interpretations. The results show that a problem related to the ambiguity of formation water resistivity can be overcome through resistivity–porosity dependencies constructed to include potential aquifer zones in the proximity of the Dacian gas-bearing reservoirs. This study also revealed and quantified uncertainties regarding the estimation of gas–water contacts from formation pressure surveys, which can be mitigated by the confirmation or correction of pressure-derived fluid contacts via the well log interpretation results. Lastly, we identified a probable resistivity logs suppression effect related both to high contents of capillary-bound water and also to the limited resolution of electrical logging tools in the presence of sand-shale thin bedding or laminations.
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Niculescu, Bogdan Mihai, and Victor Mocanu. "Characterization of Pliocene Biogenic Gas Reservoirs from the Western Black Sea Shelf (Romanian Offshore) by Integration of Well Logs and Core Data." Energies 14, no. 20 (October 14, 2021): 6629. http://dx.doi.org/10.3390/en14206629.
Full textAbstract:
The successful interpretation of open-hole well logging data relies on jointly using all available petrophysical and geological information. This paper presents relevant case studies related to the integration of well logs with core measurements for exploration wells drilled in the Romanian continental shelf area of the Western Black Sea basin. The analyzed wells targeted gas-bearing sands and silts complexes of Early Pliocene (Dacian) age, developed in a deltaic to shallow marine sedimentary environment in two distinct fields. The wireline logging programs included conventional formation evaluation logs, pressure surveys, nuclear magnetic resonance, and borehole electrical imaging logs. The core dataset comprised routine and special measurements (porosity, grain density, permeability, water saturation, and Archie parameters) carried out at quasi-reservoir confining pressure. The wireline logging suites were interpreted via a deterministic workflow, including core-derived interpretation parameters. Other core-derived parameters were used for constraining and validating the log interpretations. The results show that a problem related to the ambiguity of formation water resistivity can be overcome through resistivity–porosity dependencies constructed to include potential aquifer zones in the proximity of the Dacian gas-bearing reservoirs. This study also revealed and quantified uncertainties regarding the estimation of gas–water contacts from formation pressure surveys, which can be mitigated by the confirmation or correction of pressure-derived fluid contacts via the well log interpretation results. Lastly, we identified a probable resistivity logs suppression effect related both to high contents of capillary-bound water and also to the limited resolution of electrical logging tools in the presence of sand-shale thin bedding or laminations.
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Jurado, M. J., and M. C. Comas. "Well log interpretation and seismic character of the cenozoic sequence in the northern Alboran Sea." Geo-Marine Letters 12, no. 2-3 (June 1992): 129–36. http://dx.doi.org/10.1007/bf02084923.
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Xu, Hao, Wen Zhou, Runcheng Xie, Lina Da, Christopher Xiao, Yuming Shan, and Haotian Zhang. "Characterization of Rock Mechanical Properties Using Lab Tests and Numerical Interpretation Model of Well Logs." Mathematical Problems in Engineering 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/5967159.
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The tight gas reservoir in the fifth member of the Xujiahe formation contains heterogeneous interlayers of sandstone and shale that are low in both porosity and permeability. Elastic characteristics of sandstone and shale are analyzed in this study based on petrophysics tests. The tests indicate that sandstone and mudstone samples have different stress-strain relationships. The rock tends to exhibit elastic-plastic deformation. The compressive strength correlates with confinement pressure and elastic modulus. The results based on thin-bed log interpretation match dynamic Young’s modulus and Poisson’s ratio predicted by theory. The compressive strength is calculated from density, elastic impedance, and clay contents. The tensile strength is calibrated using compressive strength. Shear strength is calculated with an empirical formula. Finally, log interpretation of rock mechanical properties is performed on the fifth member of the Xujiahe formation. Natural fractures in downhole cores and rock microscopic failure in the samples in the cross section demonstrate that tensile fractures were primarily observed in sandstone, and shear fractures can be observed in both mudstone and sandstone. Based on different elasticity and plasticity of different rocks, as well as the characteristics of natural fractures, a fracture propagation model was built.
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Wibowo, Handoko Teguh, Bambang Prastistho, C. Prasetyadi, and Dwi Fitri Yudiantoro. "Reanalysis LUSI Stratigraphic Based on Cutting of Banjarpanji#1 Well." Journal of Earth and Marine Technology (JEMT) 3, no. 1 (October 12, 2022): 11–18. http://dx.doi.org/10.31284/j.jemt.2022.v3i1.3602.
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The subsurface configuration or stratigraphy of the Sidoarjo Hot Mudflow (Lumpur Sidoarjo/LUSI) is used to see what formations are transported out and how the material properties can be known from the well log data of Banjarpanji#1 or BJP#1 well. There is something unusual about the BJP#1 log, namely the presence of solid volcanic sandstone at a depth of 6000-9297 ft where drilling ended. This abnormality was investigated by reanalyzing volcanic sandstone by reading the well log data which is drilling data during the exploration of the Banjarpanji-1 well by PT. Lapindo Brantas, Inc. which consists of rock logs as a result of predictions (prognosis) and facts found (actual) were then compared with the interpretation of the results of the investigation team with BP MIGAS 2006 which was then confirmed by petrographic and paleontological analysis. The unusual occurrence is the LUSI stratigraphic arrangement changes based on findings in volcanic sandstone sequences that were once considered part of the Kalibeng Formation of Plio-Pliestocene age, then changed to solid volcanic sandstone units of Middle Miocene age with a depositional environment in the outer neritic-upper bathyal.
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Grana, Dario, Marco Pirrone, and Tapan Mukerji. "Quantitative log interpretation and uncertainty propagation of petrophysical properties and facies classification from rock-physics modeling and formation evaluation analysis." GEOPHYSICS 77, no. 3 (May 1, 2012): WA45—WA63. http://dx.doi.org/10.1190/geo2011-0272.1.
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Formation evaluation analysis, rock-physics models, and log-facies classification are powerful tools to link the physical properties measured at wells with petrophysical, elastic, and seismic properties. However, this link can be affected by several sources of uncertainty. We proposed a complete statistical workflow for obtaining petrophysical properties at the well location and the corresponding log-facies classification. This methodology is based on traditional formation evaluation models and cluster analysis techniques, but it introduces a full Monte Carlo approach to account for uncertainty evaluation. The workflow includes rock-physics models in log-facies classification to preserve the link between petrophysical properties, elastic properties, and facies. The use of rock-physics model predictions guarantees obtaining a consistent set of well-log data that can be used both to calibrate the usual physical models used in seismic reservoir characterization and to condition reservoir models. The final output is the set of petrophysical curves with the associated uncertainty, the profile of the facies probabilities, and the entropy, or degree of confusion, related to the most probable facies profile. The full statistical approach allows us to propagate the uncertainty from data measured at the well location to the estimated petrophysical curves and facies profiles. We applied the proposed methodology to two different well-log studies to determine its applicability, the advantages of the new integrated approach, and the value of uncertainty analysis.
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Moreau, Julien, and Jean-Bernard Joubert. "Glacial sedimentology interpretation from borehole image log: Example from the Late Ordovician deposits, Murzuq Basin (Libya)." Interpretation 4, no. 2 (May 1, 2016): B1—B16. http://dx.doi.org/10.1190/int-2015-0161.1.
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In the Murzuq Basin, the Late Ordovician glaciogenic succession forms a very complex clastic reservoir system. Although the structural setting is simple, the architecture of the stratigraphic succession is particularly intricate, and conventional wireline logs display rather homogeneous signatures. However, when exposed, the glaciogenic sedimentary succession indicates a very large range of depositional environments and clear stratigraphic changes. Based on high-quality processing and interpretation of wireline microresistivity image logs over a single well, our method allows the precise recognition of the internal sedimentary structures and supports the interpretation of the depositional environments within the Late Ordovician succession. During interpretation, it is possible to draw a descriptive sedimentological log, similar to a standard log from cores or outcrops. The image log is interpreted like a regular sedimentary log and compared to an outcrop analog from the nearby outcrop area of Ghat. The success of the well analysis resides in the quality of the borehole image log, permitting the recognition of sandstone grain sizes, textures (facies), and sorting. In addition, crucial information is extracted from the identification of glacial surface and ice-flow orientations, which, combined with the recognition of major transgressive events, allows the recognition and correlation of glacial-type stratigraphy. As in the modern Pleistocene glaciation, stadial/interstadial and glacial/interglacial stages are identified from resistivity imaging of the Libyan Ordovician succession. In addition to the unprecedented potential of correlation between wells within the basin, the sedimentary information extracted from the borehole image log provides important insights on the paleogeographic context of the basin and thus on the exploration potential of the prolific Ordovician-Silurian petroleum system.
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Snyder, Donald D., and David B. Fleming. "Well logging—A 25‐year perspective." GEOPHYSICS 50, no. 12 (December 1985): 2504–29. http://dx.doi.org/10.1190/1.1441881.
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Developments in the field of well logging over the last 25 years are reviewed. Surface and borehole instrumentation have evolved significantly, taking advantage of modern digital and analog integrated circuits. Most open‐hole petroleum well logs are now recorded digitally. Digital logs are also frequently acquired in cased‐hole petroleum, mineral, and geotechnical applications. Nuclear well‐log measurements have become accepted and reliable. New measurements include borehole compensated density and neutron‐porosity, sidewall epithermal neutron‐porosity, and most recently litho‐density. The neutron decay log, developed early in the 25‐year period, has undergone a number of major improvements since its introduction. Probes which make spectral measurements of natural gamma‐ray emission, and gamma‐ray emission from neutron interactions with matter have also been developed. Resistivity measurements are now made with probes which combine three or more sensors each with different depths of investigation so that information about the borehole invasion profile can be acquired. Acoustic logging methods have expressed major developments and improvements. The compensated sonic measurement was introduced early in the period along with the cement bond logging method. Interest in measurement of shear‐wave velocity has produced new direct shear‐wave measurements as well as improved acoustic probes for full‐waveform acoustic logging. Other interesting or promising methods which have been developed or improved during the period include the borehole televiewer, the borehole gravimeter, and the nuclear magnetic resonance log. The digital computer provides powerful capabilities for well‐log analysis both at the well site and in the office. Analysis of complex sand‐shale and carbonate formations using two or more logs in a simultaneous solution of a litho‐porosity model is now routine. Powerful signal processing techniques are being applied to “deconvolve” well logs, to enhance or synthesize images of the wellbore, and to estimate or extract information from full‐waveform acoustic logs. While new or improved measurements have been introduced and log analysts now have access to powerful computers and graphic work stations, understanding of the petrophysical significance of the measurements lags behind the basic hardware measurement and interpretation technology.
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Isakova, Tatyana G., Anna S. Persidskaya, Oleg V. Khotylev, Julia A. Kotochkova, Alena D. Egorova, Tatyana F. Dyakonova, Aleksei S. Mozgovoy, et al. "Typification of the deposits of the Tyumen Formation according to the degree of hydrodynamic conditions of sedimentation to create a petrophysical model and differentiated interpretation of well log data." Georesursy 24, no. 2 (May 16, 2022): 172–85. http://dx.doi.org/10.18599/grs.2022.2.16.
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The article considers facies analysis and the application of its results for the typification of rocks of the Tyumen Formation. The aim of the article is to create algorithms for the differentiated interpretation of geophysical studies of well log data of the Tyumen formation based on a detailed study of the core. Based on core studies, as well as geological, geophysical data and field information on the Tyumen Formation of some deposits in Western Siberia, the main groups of facies were identified, reflecting the hydrodynamic activity of sedimentation conditions: group 1 – high-dynamic conditions, group 2 – transitional conditions, group 3 – low-dynamic conditions. On the basis of this typification, petrophysical models of the deposits of the Tyumen Formation were created: petrophysical functions were built with differentiation by facies zones, boundary values of petrophysical parameters were obtained to identify reservoirs of different sedimentation conditions, algorithms for differentiated interpretation of well log data were proposed, also based on test results and field geophysical survey, differences in field characteristics for different sedimentation conditions were revealed, linear oil reserves were calculated using a new petrophysical model. The development and implementation of an integrated approach to the interpretation of well log data, taking into account the conditions of sedimentation, make it possible to increase the reliability of determining the properties of the reservoirs of the Tyumen Formation, and the efficiency of calculating the oil reserves.
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Emelyanova, Irina, Chris Dyt, M. Ben Clennell, Jean-Baptiste Peyaud, and Marina Pervukhina. "Well log data analytics: overview of applications to improve subsurface characterisation." APPEA Journal 59, no. 2 (2019): 874. http://dx.doi.org/10.1071/aj18100.
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Wireline log datasets complemented with core measurements and expert interpretation are vital for accurate reservoir characterisation. In many cases, effective use of this information for predicting rock properties requires application of advanced data analytics (DA) techniques. We developed non-linear prediction models by combining data- and knowledge-driven methods. These models were used for predicting total organic carbon and electro-facies from basic wireline logs. Four DA approaches were utilised: unsupervised, supervised, semi-supervised and expert rule based. The unsupervised approach implements ensemble clustering for detecting variations in sedimentary sequences of the subsurface. The supervised approach predicts rock properties from well logs by applying ensemble learning that requires core data measurements. The semi-supervised approach builds a decision tree for iterative clustering of well logs to locate a specific facies and uses criteria determined by a petrophysicist for making decisions at each tree node whether to continue or stop the partitioning. The expert rule based approach combines clustering techniques at individual wells with an expert’s methodology of interpreting facies to determine field-wide rock characterisation. Here we overview the developed models and their applications to log data from offshore and onshore Australian wells. We discuss the deep thinking–shallow learning versus shallow thinking–deep learning approaches in reservoir modelling and highlight the importance of close collaboration of data analysts with domain experts.
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Wu, Wenting, and Dario Grana. "Integrated petrophysics and rock physics modeling for well log interpretation of elastic, electrical, and petrophysical properties." Journal of Applied Geophysics 146 (November 2017): 54–66. http://dx.doi.org/10.1016/j.jappgeo.2017.09.007.
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Yan, P. A., and E. M. Khabarov. "WELL LOG DATA INTERPRETATION IN PALEOGEOGRAPHIC RECONSTRUCTIONS (IN THE CASE OF THE BATHONIAN-UPPER JURASSIC DEPOSITS OF SOUTH OF WEST SIBERIA)." Russian Journal of geophysical technologies, no. 4 (March 6, 2019): 27–38. http://dx.doi.org/10.18303/2619-1563-2018-4-4.
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According to the results of a comprehensive sedimentological study of the core and well log data, the dissection of the bat-upper Jurassic sediments of the South of the West Siberian oil and gas basin was carried out. Quasi-isochronous marked levels are identified. They are represented by transgressive surfaces, maximum flooding surfaces and extended carbonaceous layers. They are the basis of the performed correlation. A number of characteristic lithological-facies sequences diagnosed by well log data was established.