Dissertations / Theses on the topic 'Well log interpretation'

A novel data analysis approach that is automatic, self-learning and self-explained, and which provides accurate and reliable results is reported. The data analysis tool is capable of performing multivariate non-parametric regression analysis, as well as quantitative inferential analysis using predictive learning. Statistical approaches such as multiple regression or discriminant analysis are usually used to perform this kind of analysis. However, they lack universal capabilities and their success in any particular application is directly affected by the problem complexity.The approach employs the use of Artificial Neural Networks (ANNs) and Fuzzy Logic to perform the data analysis. The features of these two techniques are the means by which the developed data analysis approach has the ability to perform self-learning as well as allowing user interaction in the learning process. Further, they offer a means by which rules may be generated to assist human understanding of the learned analysis model, and so enable an analyst to include external knowledge.Two problems in the resource industry have been used to illustrate the proposed method, as these applications contain non-linearity in the data that is unknown and difficult to derive. They are well log data analysis in petroleum exploration and hydrocyclone data analysis in mineral processing. This research also explores how this proposed data analysis approach could enhance the analysis process for problems of this type.

A novel data analysis approach that is automatic, self-learning and self-explained, and which provides accurate and reliable results is reported. The data analysis tool is capable of performing multivariate non-parametric regression analysis, as well as quantitative inferential analysis using predictive learning. Statistical approaches such as multiple regression or discriminant analysis are usually used to perform this kind of analysis. However, they lack universal capabilities and their success in any particular application is directly affected by the problem complexity.The approach employs the use of Artificial Neural Networks (ANNs) and Fuzzy Logic to perform the data analysis. The features of these two techniques are the means by which the developed data analysis approach has the ability to perform self-learning as well as allowing user interaction in the learning process. Further, they offer a means by which rules may be generated to assist human understanding of the learned analysis model, and so enable an analyst to include external knowledge.Two problems in the resource industry have been used to illustrate the proposed method, as these applications contain non-linearity in the data that is unknown and difficult to derive. They are well log data analysis in petroleum exploration and hydrocyclone data analysis in mineral processing. This research also explores how this proposed data analysis approach could enhance the analysis process for problems of this type.

39 thin sections and petrophysical log data from the Skalle well in the Hammerfest Basin, in the southwestern part of the Barents Sea, have been studied to interpret lithology, and diagenesis and their effect on the reservoir quality, and to compare reservoir properties of the different reservoir units. Petrophysical log data have been calibrated for reservoir description in cases where core material is not available. The studied formations are comprised by the Stø, Fuglen, Hekkingen, Knurr, Kolje and the Lower Kolmule Formations. The Knurr and Kolje Formations have been identified and interpreted only by wire line logs, as core material was not available for those intervals.The Lower Kolmule Formation of sandstones of lithic greywacke composition, and the Stø Formation with sandstones of subarkosic arenite composition are considered as possible reservoir rocks. All the formations are water filled which is reflected by the low resistivity logs responses. The mature sandstones of the Stø Formation show high reservoir quality (high porosity and permeability) compared to the Lower Kolmule Formation. The Hekkingen Formation is a potential source rock for the Lower Kolmule Formation, as well as a seal (cap rock) for the Stø Formation. Cementation, dissolution, compaction, clay mineral authigenesis and stylolitization are the most significant diagenetic processes affecting the reservoir quality. Some other type of processes such as glauconitization and bioturbation are also common in the studied well.

Integrated analysis of well and geophysical data can provide detailed geologic interpretation of the subsurface in Osage County, Oklahoma. Systems tracts and depositional system successions can be interpreted at marginal seismic resolution using well log motif with seismic reflector character within a depositional context. Shelf-prism and subaqueous, delta-scale clinoforms of Missourian age observed in 3D seismic were interpreted with greater sequence stratigraphic detail when coupled with wireline well logs. The Late Pennsylvanian Midcontinent Sea was thought to be approximately 150 feet average depth across the southern Midcontinent during the Missourian Stage, and deepen towards the Arkoma and Anadarko Basins to the south. Here we show that the Late Pennsylvanian Midcontinent Sea floor was in water depths greater than 600 feet and sloped to the southeast, toward major, southern basins, during the Missourian Stage in Osage County. Shelf-prism and delta scale clinoforms up to 600 and 300 feet of relief, respectively, were observed in paired seismic and well log cross sections, thickness maps, and structure maps dipping northwest at 052° strike, upon a basin floor dipping southeast at 253° strike. Lithologic and sequence stratigraphic interpretation revealed a mixed carbonate-siliciclastic system comprising of delta, offshore shelf, and carbonate buildup depositional systems of mesothem, 3rd order sequence magnitude. The observed succession included: 1) falling stage to lowstand, sand-prone, subaqueous delta, 2) transgressive to highstand offshore shelf and carbonate bank, and 3) falling stage delta. The depositional sucession demonstrates how carbonate banks related spatially to terrigenous sediment input in northeastern Oklahoma during the Late Pennsylvanian because of glacio-eustasy and possible tectonism.

Um método para a detecção automática de tipos litológicos e contato entre camadas foi desenvolvido através de uma combinação de análise estatística de um conjunto de perfis geofísicos de poços convencionais, calibrado por descrições sistemáticas de testemunhos. O objetivo deste projeto é permitir a integração de dados de rocha em modelos de reservatório. Os testemunhos são descritos com o suporte de um sistema de nomenclatura baseado em ontologias que formaliza extensamente uma grande gama de atributos de rocha. As descrições são armazenadas em um banco de dados relacional junto com dados de perfis de poço convencionais de cada poço analisado. Esta estrutura permite definir protótipos de valores de perfil combinados para cada litologia reconhecida através do cálculo de média e dos valores de variância e covariância dos valores medidos por cada ferramenta de perfilagem para cada litologia descrita nos testemunhos. O algoritmo estatístico é capaz de aprender com cada novo testemunho e valor de log adicionado ao banco de dados, refinando progressivamente a identificação litológica. A detecção de contatos litológicos é realizada através da suavização de cada um dos perfis através da aplicação de duas médias móveis de diferentes tamanhos em cada um dos perfis. Os resultados de cada par de perfis suavizados são comparados, e as posições onde as linhas se cruzam definem profundidades onde ocorrem mudanças bruscas no valor do perfil, indicando uma potencial mudança de litologia. Os resultados da aplicação desse método em cada um dos perfis são então unificados em uma única avaliação de limites litológicos Os valores de média e variância-covariância derivados da correlação entre testemunhos e perfis são então utilizados na construção de uma distribuição gaussiana n-dimensional para cada uma das litologias reconhecidas. Neste ponto, probabilidades a priori também são calculadas para cada litologia. Estas distribuições são comparadas contra cada um dos intervalos litológicos previamente detectados por meio de uma função densidade de probabilidade, avaliando o quão perto o intervalo está de cada litologia e permitindo a atribuição de um tipo litológico para cada intervalo. O método desenvolvido foi testado em um grupo de poços da bacia de Sergipe- Alagoas, e a precisão da predição atingida durante os testes mostra-se superior a algoritmos clássicos de reconhecimento de padrões como redes neurais e classificadores KNN. O método desenvolvido foi então combinado com estes métodos clássicos em um sistema multi-agentes. Os resultados mostram um potencial significante para aplicação operacional efetiva na construção de modelos geológicos para a exploração e desenvolvimento de áreas com grande volume de dados de perfil e intervalos testemunhados.
A method for the automatic detection of lithological types and layer contacts was developed through the combined statistical analysis of a suite of conventional wireline logs, calibrated by the systematic description of cores. The intent of this project is to allow the integration of rock data into reservoir models. The cores are described with support of an ontology-based nomenclature system that extensively formalizes a large set of attributes of the rocks, including lithology, texture, primary and diagenetic composition and depositional, diagenetic and deformational structures. The descriptions are stored in a relational database along with the records of conventional wireline logs (gamma ray, resistivity, density, neutrons, sonic) of each analyzed well. This structure allows defining prototypes of combined log values for each lithology recognized, by calculating the mean and the variance-covariance values measured by each log tool for each of the lithologies described in the cores. The statistical algorithm is able to learn with each addition of described and logged core interval, in order to progressively refine the automatic lithological identification. The detection of lithological contacts is performed through the smoothing of each of the logs by the application of two moving means with different window sizes. The results of each pair of smoothed logs are compared, and the places where the lines cross define the locations where there are abrupt shifts in the values of each log, therefore potentially indicating a change of lithology. The results from applying this method to each log are then unified in a single assessment of lithological boundaries The mean and variance-covariance data derived from the core samples is then used to build an n-dimensional gaussian distribution for each of the lithologies recognized. At this point, Bayesian priors are also calculated for each lithology. These distributions are checked against each of the previously detected lithological intervals by means of a probability density function, evaluating how close the interval is to each lithology prototype and allowing the assignment of a lithological type to each interval. The developed method was tested in a set of wells in the Sergipe-Alagoas basin and the prediction accuracy achieved during testing is superior to classic pattern recognition methods such as neural networks and KNN classifiers. The method was then combined with neural networks and KNN classifiers into a multi-agent system. The results show significant potential for effective operational application to the construction of geological models for the exploration and development of areas with large volume of conventional wireline log data and representative cored intervals.

We use rock physics models for poorly consolidated rocks to diagnose reservoir sandstones in the Alvheim Field, North Sea. Geological factors that will control the rock physics and seismic properties include clay content, sorting, diagenesis, mineralogy, and bedding configuration. The various geologic factors will affect the fluid and stress sensitivity in these rocks. We investigate the interrelationships between various geological factors and seismic fluid and stress sensitivity, by combining well log data and rock physics models. Finally, we determine inter-well characteristics in terms of varying geological factors at different locations and discuss the results in terms of expected seismic signatures in the area.

This study shows how the use of modern geological investigative techniques can reopen old, “drained” hydrocarbon fields. Specifically, it looks at the White Castle Field in South Louisiana. This field has pay sections ranging from late Oligocene to late Miocene. The late Oligocene package is underexplored and understudied and contains 3 primary reservoirs (Cib Haz (CH), MW, and MR). This study established the depositional history of these reservoirs. During most of the late Oligocene, the White Castle Salt Dome was located in a minibasin on the continental slope. The CH and MW deposited in this minibasin. The CH is an amalgamation of slumped shelfal limestones, sandstones, and shales deposited during a lowstand systems tract (LST). The MW comprises a shelf-edge delta that is part of a LST. The MR is an incised valley fill located in the continental shelf that was deposited during LST after the minibasin was filled.

A seqüência mesozóica da bacia de Ucayali é a maior produtora de gás e condensado do Peru. A área do trabalho, denominada Grande Camisea, fica na parte sul da bacia e, na atualidade, pertence à companhia Plupetrol Peru Corporation. Neste trabalho, foi aplicado um método de interpretação de perfis de indução em um poço petrolífero no sector San Martin do campo Camisea (QuickLook Interpretation method). O objetivo consiste na caracterização do reservatório de San Martín utilizando um método de interpretação rápida de perfis elétricos e, assim, fornecer uma visão geral no entendimento de parâmetros de poços e reservatórios, de zonas produtivas e suas características petrofísicas de porosidade e de saturação do óleo. Para validar a interpretação, foram utilizadas a descrição geológica de testemunhos e amostras de calha, descrição e informação do sistema petrolífero do campo e a geologia regional da zona de interesse da bacia. Desta forma, foi possível apresentar uma comparação entre os valores obtidos através dos métodos detalhados executados pela Pluspetrol e o método rápido de interpretação aplicado aqui, assim como o desvio entre ambos os resultados.
The Mesozoic sequence of the Ucayali basin is the main producer of gas and condensate of Peru. The work area is called Gran Camisea, located in the south part of the basin, and, in the present time, belongs to the company Plupetrol Peru Corporation. In this work, a well log interpretation method was used in a gas well in San Martin area, part of the Camisea field. The goal is the characterization of the reservoir of San Martín using a Quick Look log interpretation method, and thus to supply a general view in the understanding of well and reservoirs parameters, productive zones and its petrophysics characteristics of porosity and saturation. To validate the interpretation, besides using the geologic description of well cores and cutting sampling, it was used the description and information of the petroleum system of Camisea gas field and its regional geology. It was possible to present a comparison between Pluspetrol values, obtained through detailed methods, and those from the Quick Look log interpretation method used here, as well as an analysis of convergence between both results.

Forward modeling and inversion plays a fundamental role in well-logging data processing and interpretation. Conventional modeling and inversion methods, however, are usually computer intensive and are not suitable for well-site applications. Neural networks provide a means for building fast and robust modeling and inversion algorithms that can be applied at well sites without calling for intensive computer resources. Several neural networks were applied and compared to interpret well logs, including layer picking, forward simulation, and inversion. A modular neural network was trained to extract the layer boundaries from a single unfocused tool response. The network showed the capability to pick layer boundaries but the confidence level was not uniformly high. Five different networks were trained with multiple tool responses to pick the layer boundaries. The results showed that the modular neural network and resilient back-propagation network could produce the most accurate results. The picked layer boundaries using multiple tool responses were located at a higher confidence level and less noise than using a single response tool. Fast forward modeling was performed with a modular neural network. The trained neural networks indicated that the modular neural network could predict the forward responses with an average accuracy of above 95%. An error analysis suggested that the neural network errors could be approximately described by a Gaussian distribution. A sensitivity test was also investigated to analyze how the errors would propagate back to the formation resistivity estimations. Larger errors were produced in conductive and thin layers, but smaller errors in thick layers. A pattern recognition-based fast forward modeling was developed. A self-organizing network was employed to classify the whole data population into different classes. Twenty percent of the training patterns from each prototype class were used for training. A modular neural network was investigated to invert Geonics EM39 induction logs. Four sub-networks were generated based on the pattern of resistivities in three-layer models. The well logging curves were subdivided into segments, which represented three-layer models, and each sub-network estimated the resistivity and thickness of every layer. The network was tested by synthetic and field data and the results were very encouraging.

Borehole imaging is one of the fastest and most precise methods for collecting subsurface data that provides high resolution information on layering, texture and dips, permitting a core-like description of the subsurface. Although the range of information recoverable from this technology is widely acknowledged, image logs are still used in a strictly qualitative manner. Interpreting image logs manually is cumbersome, time consuming and is subjective based on the experience of the interpreter. This thesis outlines new methods that automate image log interpretation and extract subsurface lithofacies information in a quantitative manner. We developed two methodologies based on advanced image analysis techniques successfully employed in remote sensing and medical imaging. The first one is a pixelbased pattern recognition technique applying textural analysis to quantify image textural properties. These properties together with standard logs and core-derived lithofacies information are used to train a back propagation Neural Network. In principle the trained and tested Neural Network is applicable for automated borehole image interpretation from similar geological settings. However, this pixel-based approach fails to make use explicitly of the spatial characteristics of a high resolution image. TAT second methodology is introduced which groups identical neighbouring pixels into objects. The resultant spectrally and spatially consistent objects are then related to geologically meaningful groups such as lithofacies by employing fuzzy classifiers. This method showed better results and is applied to outcrop photos, core photos and image logs, including a ‘difficult’ data set from a deviated well. The latter image log did not distinguish some of the conductive and resistive regions, as observed from standard logs and core photos. This is overcome by marking bed boundaries using standard logs. Bed orientations were estimated using an automated sinusoid fitting algorithm within a formal uncertainty framework in order to distinguish dipping beds and horizontal stratification. Integration of these derived logs in the methodology yields a complete automated lithofacies identification, even from the difficult dataset. The results were validated through the interpretation of cored intervals by a geologist. This is a supervised classification method which incorporates the expertise of one or several geologists, and hence includes human logic, reasoning, and current knowledge of the field heterogeneity. By including multiple geologists in the training, the results become less dependent on each individual’s subjectivity and prior experience. The method is also easily adaptable to other geological settings. In addition, it is applicable to several kinds of borehole images, for example wireline electrical borehole wall images, core photographs, and logging-while-drilling (LWD) images. Thus, the theme of this dissertation is the development of methodologies which makes image log interpretation simpler, faster, less subjective, and efficient such that it can be applied to large quantities of data.

>Magister Scientiae - MSc
The evaluation of petroleum reservoirs has shifted from single approach to an integrated approach. The integration, analysis and understanding of all available data from the well bore and creating property models is an exceptional way to characterize a reservoir. Formulating, implementing, and demonstrating the applicability of the joint inversion of seismic and well-bore related observations, and the use of information about the relationship between porosity and permeability as the key parameters for identifying the rock types and reservoir characterization is a vital approach in this study. Correlating well and seismic data, potential reservoirs can be delineated and important horizons (markers) can be pointed out to better characterize the reservoir. This thesis aims to evaluate the potential petroleum reservoirs of the Wells K-A1, K-A2, K-A3 and K-H1 of the Shungu Shungu field in the Orange Basin through the integration and comparison of results from core analysis, wireline logs and seismic and attempt to produce a good reservoir model and by additionally utilizing Petrophysics and seismic and trying to better understand why the area has dry wells. Different rock types that comprise reservoir and non reservoirs are identified in the study and five Facie types are distinguished. Tight, fine grained sandstones with low porosity values ranging from 3% - 6% where dominant in the targeted sandstone layers. Porosity values ranging from 11% - 18% where identified in the massive sandstone lithologies which where hosted by Well’s K-A2 and K-A3. Low permeability values reaching 0.1mD exist throughout the study area. Areas with high porosity also host high water saturation values ranging from 70 – 84%. An improvement in the porosity values at deeper zones (3700m -3725m) and is apparent. Poroperm plots exhibit quartz cemented sandstones and density with neutron plot suggest that the sandstones in the area contain quarts and dolomite mineralization.Well K-A3, consist of a cluster by quartzitic sandstone, meaning there is a large amount of sandstone present. There are apparent high porosity values around the sandstone. What is apparent from this plot is that there are many clusters that are scattered outside the chart. This could suggest some gas expulsions within this Well. Sandstones within the 14B2t1 to 14At1 interval are less developed in the vicinity covered by well K-A2 than at the K-A1 well location. The main targeted sandstones belong to the lower cretaceous and lie just below 13At1. The four wells drilled in this area are dry wells. The areas/blocks surrounding this area have shown to possess encouraging gas shows and a comparative study could reveal better answers. At deeper zones of the well at an interval of 5350m -5750m, there are more developed sandstones with good porosity values. The volume of shale is low and so is the water saturation. The main target sandstones in the study area are the Lower Cretaceous sandstones which are at an interval 13At1. These sandstones are not well developed but from the property model of the target surface it can be seen that the porosity values are much more improved than the average values applied on all the zones on the 3D grid.

There has been a dramatic increase in both the amount and type of geophysical well log data acquired in gas hydrate saturated rocks. Data has been acquired in both offshore and Arctic environments; its availability has shed light on the applicability of current tools and the potential usefulness of recently developed and developing technologies. Some of the more interesting areas of interest are related to the usefulness of nuclear elemental spectroscopy data and the comparison of thermal and epithermal neutron porosity measurements, the measurement of in-situ permeability, the interpretation of electrical borehole image and borehole sonic data. A key parameter for reservoir characterization and simulation is formation permeability. A reasonable understanding of this property is key to the development of future gas hydrate production. Typical applications of borehole image data are an appreciation of a reservoir’s geological environment. In hydrate saturated reservoirs, borehole images can also be used to assist in the understanding of the gas migratory path to the hydrate bearing formation. This paper presents a review of some of the current state of the art geophysical log measurements and their application in hydrate saturated reservoirs..

Rock type is a key concept in modern reservoir characterization that straddles multiple scales and bridges multiple disciplines. Reservoir rock classification (or simply rock typing) has been recognized as one of the most effective description tools to facilitate large-scale reservoir modeling and simulation. This dissertation aims to integrate core data and well logs to enhance reservoir description by classifying reservoir rocks in a geologically and petrophysically consistent manner. The main objective is to develop scientific approaches for utilizing multi-physics rock data at different time and length scales to describe reservoir rock-fluid systems. Emphasis is placed on transferring physical understanding of rock types from limited ground-truthing core data to abundant well logs using fast log simulations in a multi-layered earth model. Bimodal log-normal pore-size distribution functions derived from mercury injection capillary pressure (MICP) data are first introduced to characterize complex pore systems in carbonate and tight-gas sandstone reservoirs. Six pore-system attributes are interpreted and integrated to define petrophysical orthogonality or dissimilarity between two pore systems of bimodal log-normal distributions. A simple three-dimensional (3D) cubic pore network model constrained by nuclear magnetic resonance (NMR) and MICP data is developed to quantify fluid distributions and phase connectivity for predicting saturation-dependent relative permeability during two-phase drainage. There is rich petrophysical information in spatial fluid distributions resulting from vertical fluid flow on a geologic time scale and radial mud-filtrate invasion on a drilling time scale. Log attributes elicited by such fluid distributions are captured to quantify dynamic reservoir petrophysical properties and define reservoir flow capacity. A new rock classification workflow that reconciles reservoir saturation-height behavior and mud-filtrate for more accurate dynamic reservoir modeling is developed and verified in both clastic and carbonate fields. Rock types vary and mix at the sub-foot scale in heterogeneous reservoirs due to depositional control or diagenetic overprints. Conventional well logs are limited in their ability to probe the details of each individual bed or rock type as seen from outcrops or cores. A bottom-up Bayesian rock typing method is developed to efficiently test multiple working hypotheses against well logs to quantify uncertainty of rock types and their associated petrophysical properties in thinly bedded reservoirs. Concomitantly, a top-down reservoir description workflow is implemented to characterize intermixed or hybrid rock classes from flow-unit scale (or seismic scale) down to the pore scale based on a multi-scale orthogonal rock class decomposition approach. Correlations between petrophysical rock types and geological facies in reservoirs originating from deltaic and turbidite depositional systems are investigated in detail. Emphasis is placed on the cause-and-effect relationship between pore geometry and rock geological attributes such as grain size and bed thickness. Well log responses to those geological attributes and associated pore geometries are subjected to numerical log simulations. Sensitivity of various physical logs to petrophysical orthogonality between rock classes is investigated to identify the most diagnostic log attributes for log-based rock typing. Field cases of different reservoir types from various geological settings are used to verify the application of petrophysical rock classification to assist reservoir characterization, including facies interpretation, permeability prediction, saturation-height analysis, dynamic petrophysical modeling, uncertainty quantification, petrophysical upscaling, and production forecasting.
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Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.
Automatic Stratigraphic Interpretation of Oil Sand wells from well logs datasets typically involve recognizing the patterns of the well logs. This is done through classification of the well log response into relatively homogenous subgroups based on eletrofacies and lithofacies. The electrofacies based classification involves identifying clusters in the well log response that reflect ‘similar’ minerals and lithofacies within the logged interval. The identification of lithofacies relies on core data analysis which can be expensive and time consuming as against the electrofacies which are straight forward and inexpensive. To date, challenges of interpreting as well as correlating well log data has been on the increase especially when it involves numerous wellbore that manual analysis is almost impossible. This thesis investigates the possibilities for an automatic stratigraphic interpretation of an Oil Sand through statistical pattern recognition and rule-based (Artificial Intelligence) method. The idea involves seeking high density clusters in the multivariate space log data, in order to define classes of similar log responses. A hierarchical clustering algorithm was implemented in each of the wellbores and these clusters and classifies the wells in four classes that represent the lithologic information of the wells. These classes known as electrofacies are calibrated using a developed decision rules which identify four lithology -Sand, Sand-shale, Shale-sand and Shale in the gamma ray log data. These form the basis of correlation to generate a subsurface model.

The intracratonic Canning Basin, Western Australia, contains three Palaeozoic petroleum systems (The Larapintine L2, Ordovician – Silurian; The Larapintine L3 and L4, Devonian – early Carboniferous; and the Gondwannan G1 and G2, late Carboniferous – Permian). The NW-SE oriented Fitzroy Trough and Gregory Sub-basin (separated only by the Jones Arch) are the regional source kitchens, and shelfal positions updip from the Fitzroy Trough are oil productive from the Larapintine L3 and L4 petroleum system in fields such as Blina and Sundown, and more recently the Ungani field. A lack of exploration drilling in comparable shelfal positions updip from the Gregory Sub-basin is perceived to account for the absence of similar hydrocarbon discoveries. Through the use of a newly reprocessed regional 2D seismic grid and an enhanced stratigraphic framework produced from well correlations and palaeogeographic reconstructions, it is demonstrated that elements of all of the three petroleum systems are present on the Betty Terrace, Balgo Terrace and within the Billiluna Sub-basin of the northeast Canning Basin. Good quality reservoirs such as the late Devonian Knobby Sandstone (averaging 20.6% porosity and 567 mD permeability), the Tournaisian Laurel Formation (featuring up to 22% porosity), members of Visean-Sakmarian Grant Group (up to 18.5% porosity and 1015 mD permeability), and the Sakmarian Poole Sandstone (in excess of 20% porosity) were intersected in well bores, tied to 2D seismic data and mapped throughout the study area. Stratigraphy with regional seal potential (including Laurel Formation shales, the Grant Group B member, shales of the mid-Carboniferous Anderson Formation and the Permian Noonkanbah Formation) are determined from exploration wells to be present across the project area. A regional geochemical source rock assessment indicates that the Permian Noonkanbah Formation is organically rich (2.17% TOC), and that the pre-Carboniferous stratigraphy (members of Anderson Formation, 0.14% TOC; Laurel Formation, 0.56% TOC; Devonian Gogo Formation, 0.14% TOC; and the Silurian Carribuddy Group Bongabinni Member, 0.13% TOC) are organically lean. The Llanvirn Goldwyer Formation (1.5% TOC) is regionally organically rich, though is unlikely to be found in a basinal palaeogeographic setting within the study area. Thermal maturity was investigated using 1D and 2D petroleum systems models, which determined that (1) the Noonkanbah Formation is immature, and (2) that the pre-Carboniferous source rocks are mature for hydrocarbon generation, reaching peak thermal maturity in the Triassic (200 Ma). 4-way dip closures, 3-way fault bound dip closures, and horst block trapping configurations were identified on 2D seismic, but an analysis of exploratory dry holes indicates that structural closures that developed in the Carboniferous were likely reconfigured during the Triassic Fitzroy Movement, where hydrocarbons leaked out of traps. Modelling indicates that the timing of hydrocarbon generation occurred over two main periods; the Siluro-Devonian (436 Ma – 350 Ma) and in the Triassic (220 Ma – 192 Ma). It is designated that exploration within all three petroleum systems in the project area is considered to be high-risk. It is concluded from this study that the Larapintine L3 and L4 petroleum system represents the best prospectivity in positions nearest the Gregory Sub-basin.
Thesis (MPhil) -- University of Adelaide, Australian School of Petroleum, 2017

The estimation of porosity, water saturation, kerogen concentration, and mineral composition is an integral part of unconventional shale reservoir formation evaluation. Porosity, water saturation, and kerogen content determine the amount of hydrocarbon-in-place while mineral composition affects hydro-fracture generation and propagation. Effective hydraulic fracturing is a basic requirement for economically viable flow of gas in very-low permeability shales. Brittle shales are favorable for initiation and propagation of hydraulic fracture because they require marginal or no plastic deformation. By contrast, ductile shales tend to oppose fracture propagation and can heal hydraulic fractures. Silica and carbonate-rich shales often exhibit brittle behavior while clay-rich shales tend to be ductile. Many operating companies have turned their attention to neutron capture gamma-ray spectroscopy (NCS) logs for assessing in-situ mineral composition. The NCS tool converts the energy spectrum of neutron-induced captured gamma-rays into relative elemental yields and subsequently transforms them to dry-weight elemental fractions. However, NCS logs are not usually included in a well-logging suite due to cost, tool availability, and borehole conditions. Conventional well logs are typically acquired as a minimum logging program because they provide geologists and petrophysicists with the basic elements for tops identification, stratigraphic correlation, and net-pay determination. Most petrophysical interpretation techniques commonly used to quantify mineral composition from conventional well logs are based on the assumption that lithology is dominated by one or two minerals. In organic shale formations, these techniques are ineffective because all well logs are affected by large variations of mineralogy and pore structure. Even though it is difficult to separate the contribution from each mineral and fluid component on well logs using conventional interpretation methods, well logs still bear essential petrophysical properties that can be estimated using an inversion method. This thesis introduces an inversion-based workflow to estimate mineral and fluid concentrations of shale gas formations using conventional well logs. The workflow starts with the construction and calibration of a mineral model based on core analysis of crushed samples and X-Ray Diffraction (XRD). We implement a mineral grouping approach that reduces the number of unknowns to be estimated by the inversion without loss of accuracy in the representation of the main minerals. The second step examines various methods that can provide good initial values for the inversion. For example, a reliable prediction of kerogen concentration can be obtained using the ΔlogR method (Passey et al., 1990) as well as an empirical correlation with gamma-ray or uranium logs. After the mineral model is constructed and a set of initial values are established, nonlinear joint inversion estimates mineral and fluid concentrations from conventional well logs. An iterative refinement of the mineral model can be necessary depending on formation complexity and data quality. The final step of the workflow is to perform rock classification to identify favorable production zones. These zones are selected based on their hydrocarbon potential inferred from inverted petrophysical properties. Two synthetic examples with known mineral compositions and petrophysical properties are described to illustrate the application of inversion. The impact of shoulder-bed effects on inverted properties is examined for the two inversion modes: depth-by-depth and layer-by-layer. This thesis also documents several case studies from Haynesville and Barnett shales where the proposed workflow was successfully implemented and is in good agreement with core measurements and NCS logs. The field examples confirm the accuracy and reliability of nonlinear inversion to estimate porosity, water saturation, kerogen concentration, and mineral composition.
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Multi-component induction resistivity and sonic measurements acquired in high-angle wells can be strongly influenced by shoulder-bed effects, anisotropy resulting from sand-shale laminations, and presence of mud-filtrate invasion. Understanding the corresponding biasing effects aids in the interpretation of resistivity and sonic measurements and subsequently leads to more accurate and reliable formation evaluation. This dissertation describes numerical simulation studies examining the effects on multi-component induction and sonic measurements in a variety of complex formation models. Subsequently, a joint inversion scheme is presented that combines resistivity and sonic measurements to estimate in situ petrophysical and elastic properties in the presence of mud-filtrate invasion. To facilitate the simulation study of multi-component induction logs, I develop a new finite-difference algorithm for the numerical simulation of frequency-domain electromagnetic borehole measurements. The algorithm~uses a coupled scalar-vector potential formulation for arbitrary three-dimensional inhomogeneous and electrically anisotropic media. Simulations show that shoulder-bed anisotropy: enhances shoulder-bed effects across sand layers; and impacts invasion sensitivities to significantly alter the assessment of invasion in terms of invaded- and virgin-zone resistivities, radial length, and front shape. For the simulation study of sonic logs, I develop a three-dimensional, finite-difference time-domain algorithm that models elastic wave propagation in a fluid-filled borehole. Simulations show that presence of anisotropy not only alters the degree of dispersion observed in flexural and Stoneley waves, but also alters their responses to invasion. In addition, presence of a dipping shoulder bed can significantly distort flexural dispersion, making it difficult to identify the low frequency asymptote corresponding to formation shear wave velocity. Lastly, I consider a radial one-dimensional model in the development of a joint resistivity and sonic inversion algorithm. This scheme simultaneously inverts array-induction apparent conductivities and sonic flexural and Stoneley dispersions for the rock's elastic moduli and water saturation in the presence of mud-filtrate invasion. Inversions are performed on numerically simulated data for a variety of models reflecting soft and hard rock formations with presence of water- and oil-based mud-filtrate invasion. Results show the estimated invasion profiles display excellent agreement with the true models, and the elastic moduli are estimated to within a few percent of the true values.
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