R. Akkurt, Tim T. Conroy, D. Psaila, A. Paxton, Jacob Low, P. Spaans
Recent advances in data science and machine learning (ML) have brought the benefits of these technologies closer to the mainstream of petrophysics. ML systems, where decisions and self-checks are made by carefully designed algorithms, in addition to executing typical tasks such as classification and regression, offer efficient and liberating solutions to the modern petrophysicist. The outline of such a system and its application in the form of a multilevel workflow to a 59-well multifield study are presented in this paper. The main objective of the workflow is to identify outliers in bulk density and compressional slowness logs and to reconstruct them using data-driven predictive models. A secondary objective of the project is to predict shear slowness in zones where such data do not exist. The system is fully automated, designed to optimize the use of all available data, and provide uncertainty estimates. It integrates modern concepts for outlier detection, predictive classification, and regression, as well as multidimensional scaling based on inter-well similarity. Benchmarking of ML results against those created by experienced petrophysicists shows that the ML workflow can provide high-quality answers that compare favorably to those produced by human experts. A second validation exercise, that compares acoustic impedance logs computed from ML answers to actual seismic data, provides further evidence for the accuracy of the ML-generated results. The ML system supports the petrophysicist by easing the burden on repetitive and burdensome quality control tasks. The efficiency gains and time savings created can be used for enhanced effective cross-discipline integration, collaboration, and further innovation.
{"title":"An Unsupervised Machine-Learning Workflow for Outlier Detection and Log Editing With Prediction Uncertainty","authors":"R. Akkurt, Tim T. Conroy, D. Psaila, A. Paxton, Jacob Low, P. Spaans","doi":"10.30632/pjv64n2-2023a5","DOIUrl":"https://doi.org/10.30632/pjv64n2-2023a5","url":null,"abstract":"Recent advances in data science and machine learning (ML) have brought the benefits of these technologies closer to the mainstream of petrophysics. ML systems, where decisions and self-checks are made by carefully designed algorithms, in addition to executing typical tasks such as classification and regression, offer efficient and liberating solutions to the modern petrophysicist. The outline of such a system and its application in the form of a multilevel workflow to a 59-well multifield study are presented in this paper. The main objective of the workflow is to identify outliers in bulk density and compressional slowness logs and to reconstruct them using data-driven predictive models. A secondary objective of the project is to predict shear slowness in zones where such data do not exist. The system is fully automated, designed to optimize the use of all available data, and provide uncertainty estimates. It integrates modern concepts for outlier detection, predictive classification, and regression, as well as multidimensional scaling based on inter-well similarity. Benchmarking of ML results against those created by experienced petrophysicists shows that the ML workflow can provide high-quality answers that compare favorably to those produced by human experts. A second validation exercise, that compares acoustic impedance logs computed from ML answers to actual seismic data, provides further evidence for the accuracy of the ML-generated results. The ML system supports the petrophysicist by easing the burden on repetitive and burdensome quality control tasks. The efficiency gains and time savings created can be used for enhanced effective cross-discipline integration, collaboration, and further innovation.","PeriodicalId":170688,"journal":{"name":"Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117235749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Inferring subsurface structure from sparse log data is crucial for geology. Recently, deep-learning-based methods, which provide sufficient prior knowledge from training sets, have been proven to aid in sedimentary facies modeling. However, these methods suffer from suboptimal controllability of the geological model, i.e., the expected geological pattern fails to be specified, resulting in unpredictable generated geological structures. To bridge the gap, we propose a novel Exemplar-Guided Facies Modeling (EGFM) approach, which synthesizes a facies model from log data given a pattern exemplar. The key insight in EGFM is to decouple the content and pattern in the target model, where the content refers to the match with well data, and the pattern is the properties of geological structures, such as fluvial course and shape. On the basis of well data as the hard condition, a pattern exemplar is introduced as the reference model for geological realizations. In addition to preserving the commonalities of the holistic geological pattern (from the geological image set), such as structural connectivity, the pattern details of the geological realization can be tuned through pattern exemplars. Moreover, we introduce an adaptive feature fusion block (AFB) to adaptively fuse the content and pattern features for more natural results. Extensive experimental results on two river data sets demonstrate that our proposed EGFM for conditional facies modeling achieves satisfying visual quality and pattern controllability.
{"title":"Exemplar-Guided Sedimentary Facies Modeling for Bridging Pattern Controllability Gap","authors":"Chunlei Wu, Fei Hu, Di Sun, Liqiang Zhang, Leiquan Wang, Huan Zhang","doi":"10.30632/pjv64n2-2023a8","DOIUrl":"https://doi.org/10.30632/pjv64n2-2023a8","url":null,"abstract":"Inferring subsurface structure from sparse log data is crucial for geology. Recently, deep-learning-based methods, which provide sufficient prior knowledge from training sets, have been proven to aid in sedimentary facies modeling. However, these methods suffer from suboptimal controllability of the geological model, i.e., the expected geological pattern fails to be specified, resulting in unpredictable generated geological structures. To bridge the gap, we propose a novel Exemplar-Guided Facies Modeling (EGFM) approach, which synthesizes a facies model from log data given a pattern exemplar. The key insight in EGFM is to decouple the content and pattern in the target model, where the content refers to the match with well data, and the pattern is the properties of geological structures, such as fluvial course and shape. On the basis of well data as the hard condition, a pattern exemplar is introduced as the reference model for geological realizations. In addition to preserving the commonalities of the holistic geological pattern (from the geological image set), such as structural connectivity, the pattern details of the geological realization can be tuned through pattern exemplars. Moreover, we introduce an adaptive feature fusion block (AFB) to adaptively fuse the content and pattern features for more natural results. Extensive experimental results on two river data sets demonstrate that our proposed EGFM for conditional facies modeling achieves satisfying visual quality and pattern controllability.","PeriodicalId":170688,"journal":{"name":"Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124885028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jose J. Salazar, Jesus Ochoa, LeAnne Garland, L. Lake, M. Pyrcz
Data analytics facilitate the examination of spatial data sets by using multiple techniques to find and understand patterns to guide decision making. However, standard data analysis tools assume that the data are independent and identically distributed, an assumption that spatial data sets usually do not fulfill. Furthermore, the usual methods neglect spatial continuity and the inherent data paucity that should be considered in the data analytics workflow. We present a new approach that combines data analytics, geostatistics, and optimization techniques to provide an end-to-end workflow to analyze two-dimensional (2D) data sets. The proposed workflow identifies outliers based on their spatial location or distribution, models geological trends using a Gaussian kernel, models the semivariogram, and performs sequential Gaussian simulation applying kriging or cokriging for cosimulation. Moreover, it provides metrics and diagnostic plots to evaluate the goodness of the results at each step. It is also semiautomatic because it leverages the user’s judgment for subsequent operations. For optimization, the workflow uses Bayesian optimization and evolutionary algorithms. We demonstrate the use of the workflow by analyzing 1,152 wells over the Duvernay Formation in Canada. The examples include the simulation of density-porosity as the secondary feature and the cosimulation of total organic content constrained by the former. The proposed workflow helps focus more on interpreting the results than the modeling parameters, reducing workforce time and subjective errors. Moreover, the spatial simulation includes multiple realizations to assess uncertainty and support decision making in data paucity scenarios. Overall, the proposed workflow is a valuable and complementary tool for evaluating uncertainty in mature geospatial data.
{"title":"Spatial Data Analytics-Assisted Subsurface Modeling: A Duvernay Case Study","authors":"Jose J. Salazar, Jesus Ochoa, LeAnne Garland, L. Lake, M. Pyrcz","doi":"10.30632/pjv64n2-2023a9","DOIUrl":"https://doi.org/10.30632/pjv64n2-2023a9","url":null,"abstract":"Data analytics facilitate the examination of spatial data sets by using multiple techniques to find and understand patterns to guide decision making. However, standard data analysis tools assume that the data are independent and identically distributed, an assumption that spatial data sets usually do not fulfill. Furthermore, the usual methods neglect spatial continuity and the inherent data paucity that should be considered in the data analytics workflow. We present a new approach that combines data analytics, geostatistics, and optimization techniques to provide an end-to-end workflow to analyze two-dimensional (2D) data sets. The proposed workflow identifies outliers based on their spatial location or distribution, models geological trends using a Gaussian kernel, models the semivariogram, and performs sequential Gaussian simulation applying kriging or cokriging for cosimulation. Moreover, it provides metrics and diagnostic plots to evaluate the goodness of the results at each step. It is also semiautomatic because it leverages the user’s judgment for subsequent operations. For optimization, the workflow uses Bayesian optimization and evolutionary algorithms. We demonstrate the use of the workflow by analyzing 1,152 wells over the Duvernay Formation in Canada. The examples include the simulation of density-porosity as the secondary feature and the cosimulation of total organic content constrained by the former. The proposed workflow helps focus more on interpreting the results than the modeling parameters, reducing workforce time and subjective errors. Moreover, the spatial simulation includes multiple realizations to assess uncertainty and support decision making in data paucity scenarios. Overall, the proposed workflow is a valuable and complementary tool for evaluating uncertainty in mature geospatial data.","PeriodicalId":170688,"journal":{"name":"Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115297548","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, a supervised machine-learning (ML) method to remove artifacts and noise from borehole images is described. Borehole images may exhibit a variety of issues and artifacts due to reasons such as environmental and thermal noise, imperfect calibration, and current leakage through the tool body. Methods that are currently employed to improve these images are based on traditional signal-processing techniques. Although these methods are capable of removing the artifacts in images and significantly improving image quality, they have some drawbacks as well. These drawbacks include not being entirely suitable for real-time implementation and issues with reproducibility. The alternative method presented here is based on an ML algorithm that is trained using a data set pairing raw data with data processed using a traditional signal-processing-based approach. The resulting ML model is capable of being implemented in near-real time. Furthermore, the application of the algorithm does not require user supervision, increasing the reproducibility of the results.
{"title":"Removal of Artifacts in Borehole Images Using Machine Learning","authors":"B. Guner, A. Fouda, P. Barrett","doi":"10.30632/pjv64n2-2023a6","DOIUrl":"https://doi.org/10.30632/pjv64n2-2023a6","url":null,"abstract":"In this paper, a supervised machine-learning (ML) method to remove artifacts and noise from borehole images is described. Borehole images may exhibit a variety of issues and artifacts due to reasons such as environmental and thermal noise, imperfect calibration, and current leakage through the tool body. Methods that are currently employed to improve these images are based on traditional signal-processing techniques. Although these methods are capable of removing the artifacts in images and significantly improving image quality, they have some drawbacks as well. These drawbacks include not being entirely suitable for real-time implementation and issues with reproducibility. The alternative method presented here is based on an ML algorithm that is trained using a data set pairing raw data with data processed using a traditional signal-processing-based approach. The resulting ML model is capable of being implemented in near-real time. Furthermore, the application of the algorithm does not require user supervision, increasing the reproducibility of the results.","PeriodicalId":170688,"journal":{"name":"Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124113511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sonic well logs provide critical information to calibrate seismic data and support geomechanical characterization. Advanced subsurface data analytics and machine learning enable new methods and workflows for property estimation, regression, and classification for geoscience and subsurface engineering applications. However, current applications for imputation of well-logging values rely only on model accuracy and low error predictions. T raditional model validation techniques are not enough to validate models and account for the substantial uncertainty in the subsurface. Well-logging imputation estimates and their associated uncertainty models are essential to the field development planning and decision-making workflows, such as reservoir modeling, volumetric resource assessment, predrill prediction with uncertainty, remaining resource mapping, and production allocation. When performing subsurface feature imputation with machine learning, we must expand our machine-learning model training and complexity tuning workflows to check the entire uncertainty model to ensure uncertainty distributions are precise and accurate. We propose a workflow that integrates the goodness metric to calculate accurate and precise uncertainty models of sonic well-log predictions based on ensembles of the machine-learning estimates. Our workflow combines model evaluation and visualization of the estimates and the uncertainty model with respect to measured depth. Our proposed method provides intuitive diagnostics and metrics to evaluate estimation accuracy and uncertainty model goodness.
{"title":"Sonic Well-Log Imputation Through Machine-Learning-Based Uncertainty Models","authors":"Eduardo Maldonado-Cruz, J. Foster, M. Pyrcz","doi":"10.30632/pjv64n2-2023a7","DOIUrl":"https://doi.org/10.30632/pjv64n2-2023a7","url":null,"abstract":"Sonic well logs provide critical information to calibrate seismic data and support geomechanical characterization. Advanced subsurface data analytics and machine learning enable new methods and workflows for property estimation, regression, and classification for geoscience and subsurface engineering applications. However, current applications for imputation of well-logging values rely only on model accuracy and low error predictions. T raditional model validation techniques are not enough to validate models and account for the substantial uncertainty in the subsurface. Well-logging imputation estimates and their associated uncertainty models are essential to the field development planning and decision-making workflows, such as reservoir modeling, volumetric resource assessment, predrill prediction with uncertainty, remaining resource mapping, and production allocation. When performing subsurface feature imputation with machine learning, we must expand our machine-learning model training and complexity tuning workflows to check the entire uncertainty model to ensure uncertainty distributions are precise and accurate. We propose a workflow that integrates the goodness metric to calculate accurate and precise uncertainty models of sonic well-log predictions based on ensembles of the machine-learning estimates. Our workflow combines model evaluation and visualization of the estimates and the uncertainty model with respect to measured depth. Our proposed method provides intuitive diagnostics and metrics to evaluate estimation accuracy and uncertainty model goodness.","PeriodicalId":170688,"journal":{"name":"Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132009620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Karthigan Sinnathamby, Chang-Yu Hou, V. Gkortsas, Lalitha Venkataramanan, H. Datir, T. Kollien, F. Fleuret
Electrofacies using well logs play a vital role in reservoir characterization. Often, they are sorted into clusters according to the self-similarity of input logs and do not capture the known underlying physical process. In this paper, we propose an unsupervised clustering algorithm based on the concept of dynamic programming, in which the underlying physical processes and geological constraints, such as the number of clusters, number of transitions between clusters, and minimal size of formation layers, can be directly integrated. We benchmark the proposed algorithm with synthetic data sets and demonstrate its applications to two field examples, where formations are clustered into zones through automated clustering using a consistent resistivity response. The inputs for our examples are porosity, clay volume fraction from elemental analysis, invaded zone resistivity, and invaded zone water saturation from dielectric interpretation or nuclear magnetic resonance logs. The proposed algorithm provides the optimized cluster pattern/electrofacies that satisfies desired constraints and enables the extraction of relevant petrophysical parameters, such as brine resistivity, cementation, and saturation exponents, as well as parameters that relate to the cation exchange capacity (CEC) of the clay for shaly-sand formations. Beyond the immediate examples demonstrated in this paper, we present the proposed algorithm in a generic form such that it can be easily tailored to the task at hand, taking into account any prior knowledge of the physics of the underlying process.
{"title":"Unsupervised Electrofacies Clustering Based on Parameterization of Petrophysical Properties: A Dynamic Programming Approach","authors":"Karthigan Sinnathamby, Chang-Yu Hou, V. Gkortsas, Lalitha Venkataramanan, H. Datir, T. Kollien, F. Fleuret","doi":"10.30632/pjv64n2-2023a1","DOIUrl":"https://doi.org/10.30632/pjv64n2-2023a1","url":null,"abstract":"Electrofacies using well logs play a vital role in reservoir characterization. Often, they are sorted into clusters according to the self-similarity of input logs and do not capture the known underlying physical process. In this paper, we propose an unsupervised clustering algorithm based on the concept of dynamic programming, in which the underlying physical processes and geological constraints, such as the number of clusters, number of transitions between clusters, and minimal size of formation layers, can be directly integrated. We benchmark the proposed algorithm with synthetic data sets and demonstrate its applications to two field examples, where formations are clustered into zones through automated clustering using a consistent resistivity response. The inputs for our examples are porosity, clay volume fraction from elemental analysis, invaded zone resistivity, and invaded zone water saturation from dielectric interpretation or nuclear magnetic resonance logs. The proposed algorithm provides the optimized cluster pattern/electrofacies that satisfies desired constraints and enables the extraction of relevant petrophysical parameters, such as brine resistivity, cementation, and saturation exponents, as well as parameters that relate to the cation exchange capacity (CEC) of the clay for shaly-sand formations. Beyond the immediate examples demonstrated in this paper, we present the proposed algorithm in a generic form such that it can be easily tailored to the task at hand, taking into account any prior knowledge of the physics of the underlying process.","PeriodicalId":170688,"journal":{"name":"Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128629685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-01DOI: 10.30632/pjv64n2-2023a10
We built a deconvolution model for induction log data using machine learning (ML). Unlike iterative forward modeling inversion methods, the deconvolution model is extremely fast. Unlike linear deconvolution models in the past, ML-based deconvolution finds accurate layer resistivity and layer boundaries. For a unit induction tool 2C40, the 21-point, 10-ft window deconvolution model works satisfactorily.
{"title":"Machine-Learning-Based Deconvolution Method Provides High-Resolution Fast Inversion of Induction Log Data","authors":"","doi":"10.30632/pjv64n2-2023a10","DOIUrl":"https://doi.org/10.30632/pjv64n2-2023a10","url":null,"abstract":"We built a deconvolution model for induction log data using machine learning (ML). Unlike iterative forward modeling inversion methods, the deconvolution model is extremely fast. Unlike linear deconvolution models in the past, ML-based deconvolution finds accurate layer resistivity and layer boundaries. For a unit induction tool 2C40, the 21-point, 10-ft window deconvolution model works satisfactorily.","PeriodicalId":170688,"journal":{"name":"Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128417154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
R. Pierpont, Kristoffer Birkeland, A. Cely, T. Yang, Li Chen, V. Achourov, S. Betancourt, Jesus A. Cañas, Julia C. Forsythe, A. Pomerantz, Jing Yang, H. Datir, O. Mullins
Two adjacent reservoirs in offshore oil fields have been evaluated using extensive data acquisition across multiple disciplines; several surprising observations were made. Differing levels of biodegradation were measured in the nearly adjacent reservoirs, yet related standard geochemical markers are contradictory. Unexpectedly, the more biodegraded oil had less asphaltene content, and this reservoir had some heavy end deposition in the core but upstructure, not at the oil-water contact (OWC) as would be expected, especially with biodegradation. Wax appears to be an issue in the nonbiodegraded oil. These many puzzling observations, along with unclear connectivity, gave rise to uncertainties about field development planning. Combined petroleum systems and reservoir fluid geodynamic considerations resolved the observations into a single, self-consistent geo-scenario, the co-evolution of reservoir rock and fluids in geologic time. A spill-fill sequence of trap filling with biodegradation helps explain differences in biodegradation and wax content. A subsequent, recent charge of condensate, stacked in one fault block and mixed in the target oil reservoir in the second fault block, explains conflicting metrics of biodegradation between C7 vs. C16 indices. Asphaltene instability and deposition at the upstructure contact between the condensate and black oil, and the motion of this contact during condensate charge, explain heavy end deposition in core. Moreover, this process accounts for asphaltene dilution and depletion in the corresponding oil. Downhole fluid analysis (DFA) asphaltene gradients and variations in geochemical markers with seismic imaging clarify likely connectivity in these reservoirs. The geo-scenario provides a benchmark of comparison for all types of reservoir data and readily projects into production concerns. The initial apparent puzzles of this oil field have been resolved with a robust understanding of the corresponding reservoirs and development strategies.
{"title":"Enigmatic Reservoir Properties Deciphered Using Petroleum System Modeling and Reservoir Fluid Geodynamics","authors":"R. Pierpont, Kristoffer Birkeland, A. Cely, T. Yang, Li Chen, V. Achourov, S. Betancourt, Jesus A. Cañas, Julia C. Forsythe, A. Pomerantz, Jing Yang, H. Datir, O. Mullins","doi":"10.30632/pjv64n1-2023a1","DOIUrl":"https://doi.org/10.30632/pjv64n1-2023a1","url":null,"abstract":"Two adjacent reservoirs in offshore oil fields have been evaluated using extensive data acquisition across multiple disciplines; several surprising observations were made. Differing levels of biodegradation were measured in the nearly adjacent reservoirs, yet related standard geochemical markers are contradictory. Unexpectedly, the more biodegraded oil had less asphaltene content, and this reservoir had some heavy end deposition in the core but upstructure, not at the oil-water contact (OWC) as would be expected, especially with biodegradation. Wax appears to be an issue in the nonbiodegraded oil. These many puzzling observations, along with unclear connectivity, gave rise to uncertainties about field development planning. Combined petroleum systems and reservoir fluid geodynamic considerations resolved the observations into a single, self-consistent geo-scenario, the co-evolution of reservoir rock and fluids in geologic time. A spill-fill sequence of trap filling with biodegradation helps explain differences in biodegradation and wax content. A subsequent, recent charge of condensate, stacked in one fault block and mixed in the target oil reservoir in the second fault block, explains conflicting metrics of biodegradation between C7 vs. C16 indices. Asphaltene instability and deposition at the upstructure contact between the condensate and black oil, and the motion of this contact during condensate charge, explain heavy end deposition in core. Moreover, this process accounts for asphaltene dilution and depletion in the corresponding oil. Downhole fluid analysis (DFA) asphaltene gradients and variations in geochemical markers with seismic imaging clarify likely connectivity in these reservoirs. The geo-scenario provides a benchmark of comparison for all types of reservoir data and readily projects into production concerns. The initial apparent puzzles of this oil field have been resolved with a robust understanding of the corresponding reservoirs and development strategies.","PeriodicalId":170688,"journal":{"name":"Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121869068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Dernaika, S. Masalmeh, Bashar Mansour, Osama Al Jallad, S. Koronfol
In carbonate reservoirs, permeability prediction is often difficult due to the influence of various geological variables that control fluid flow. Many attempts have been made to estimate permeability from porosity by using theoretical and empirical equations. The suggested permeability models have been questionable in carbonates due to inherent heterogeneity and complex pore systems. The main objective of this paper is to provide a workflow to improve the use of existing models (e.g., Kozeny, Lucia, and Winland) to predict permeability in carbonate reservoirs. More than 1,000 core plugs were studied from seven different carbonate reservoirs across the Middle East: mainly Cretaceous reservoirs. The plugs were carefully selected to represent a wide range of properties within the cored intervals. The data set available included laboratory-measured helium porosity, gas permeability, thin-section photomicrographs, and high-pressure mercury injection. Rock textures were analyzed in the thin-section photomicrographs and were classified based on their content as grainy, muddy, and mixed. Special attention was given to the diagenesis effects, mainly compaction, cementation, and dissolution. The texture information was plotted in the porosity-permeability domain and was found to produce three distinct porosity-permeability relationships. Each texture gave a unique porosity-permeability trend, where the extent of the trend was controlled by diagenesis. Rock types were defined on each trend by detailed texture analysis and capillary pressure. Three different permeability equations (Kozeny, Winland, and Lucia) were evaluated to study their effectiveness in complex carbonate reservoirs. Both Kozeny and Lucia models honored the geology of the samples and showed similar trends to the porosity-permeability relationships, whereas the Winland model gave different slopes to the experimental data. The prediction of the permeability was improved by using different model parameters per RRT within each texture. This work presents a systematic approach to construct correlations between porosity and permeability in complex carbonate reservoirs. Model parameters (i.e., FZI, RFN, and r35) were suggested within different geological rock types to estimate permeability. Based on the workflow presented in the paper, the predicted permeability was improved to less than a factor of 2 compared to the measured values. Moreover, the same workflow was applied using the data from seven different reservoirs, and the same rock typing scheme was applicable to all the reservoirs. Such work is not abundant in the literature and would serve to improve permeability prediction in heterogeneous carbonate reservoirs, which is one of the main uncertainties in modeling carbonates.
{"title":"Modeling Permeability in Different Carbonate Rock Types","authors":"M. Dernaika, S. Masalmeh, Bashar Mansour, Osama Al Jallad, S. Koronfol","doi":"10.30632/pjv64n1-2023a2","DOIUrl":"https://doi.org/10.30632/pjv64n1-2023a2","url":null,"abstract":"In carbonate reservoirs, permeability prediction is often difficult due to the influence of various geological variables that control fluid flow. Many attempts have been made to estimate permeability from porosity by using theoretical and empirical equations. The suggested permeability models have been questionable in carbonates due to inherent heterogeneity and complex pore systems. The main objective of this paper is to provide a workflow to improve the use of existing models (e.g., Kozeny, Lucia, and Winland) to predict permeability in carbonate reservoirs. More than 1,000 core plugs were studied from seven different carbonate reservoirs across the Middle East: mainly Cretaceous reservoirs. The plugs were carefully selected to represent a wide range of properties within the cored intervals. The data set available included laboratory-measured helium porosity, gas permeability, thin-section photomicrographs, and high-pressure mercury injection. Rock textures were analyzed in the thin-section photomicrographs and were classified based on their content as grainy, muddy, and mixed. Special attention was given to the diagenesis effects, mainly compaction, cementation, and dissolution. The texture information was plotted in the porosity-permeability domain and was found to produce three distinct porosity-permeability relationships. Each texture gave a unique porosity-permeability trend, where the extent of the trend was controlled by diagenesis. Rock types were defined on each trend by detailed texture analysis and capillary pressure. Three different permeability equations (Kozeny, Winland, and Lucia) were evaluated to study their effectiveness in complex carbonate reservoirs. Both Kozeny and Lucia models honored the geology of the samples and showed similar trends to the porosity-permeability relationships, whereas the Winland model gave different slopes to the experimental data. The prediction of the permeability was improved by using different model parameters per RRT within each texture. This work presents a systematic approach to construct correlations between porosity and permeability in complex carbonate reservoirs. Model parameters (i.e., FZI, RFN, and r35) were suggested within different geological rock types to estimate permeability. Based on the workflow presented in the paper, the predicted permeability was improved to less than a factor of 2 compared to the measured values. Moreover, the same workflow was applied using the data from seven different reservoirs, and the same rock typing scheme was applicable to all the reservoirs. Such work is not abundant in the literature and would serve to improve permeability prediction in heterogeneous carbonate reservoirs, which is one of the main uncertainties in modeling carbonates.","PeriodicalId":170688,"journal":{"name":"Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133458161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
C. P. Ezenkwu, John Guntoro, A. Starkey, V. Vaziri, Maurillio Addario
Well logging has been an integral part of decision making at different stages (drilling, completion, production, abandonment) of a well’s history. However, the traditional human-reliant approach to well-log interpretation, which has been the most common practice in the industry, can be time consuming, subjective, and incapable of identifying fine details in log curves. Previous studies have recommended automated approaches as a candidate for addressing these challenges. Despite the progress made so far, what is not yet clear from the existing literature is the extent to which these automated approaches can dispense with human interventions in real-life scenarios. This paper presents an empirical review of different depth-matching techniques in real-life timelapse well logs, primarily focusing on gamma ray and the extent to which the outcomes of these techniques match the results from a human expert. Specifically, the performances of dynamic time warping (DTW), constrained DTW (CDTW), and correlation optimized warping (COW) are investigated. The experiments also consider the effects of filtering and normalization on the performance of each of the techniques. Concerning the correlations of each technique’s outcome with the reference data and an expert-generated outcome, this research identifies and discusses its key challenges, as well as provides recommendations for future research directions. Although the COW technique has its limitations, as discussed in this paper, our experiments demonstrate that it shows more potential than DTW and its variants in the well-log pattern alignment task. The work entailed by this research is significant because identifying and discussing the limitations of these techniques is vital for solution-oriented future research in this area.
{"title":"Automated Well-Log Pattern Alignment and Depth-Matching Techniques: An Empirical Review and Recommendations","authors":"C. P. Ezenkwu, John Guntoro, A. Starkey, V. Vaziri, Maurillio Addario","doi":"10.30632/pjv64n1-2023a9","DOIUrl":"https://doi.org/10.30632/pjv64n1-2023a9","url":null,"abstract":"Well logging has been an integral part of decision making at different stages (drilling, completion, production, abandonment) of a well’s history. However, the traditional human-reliant approach to well-log interpretation, which has been the most common practice in the industry, can be time consuming, subjective, and incapable of identifying fine details in log curves. Previous studies have recommended automated approaches as a candidate for addressing these challenges. Despite the progress made so far, what is not yet clear from the existing literature is the extent to which these automated approaches can dispense with human interventions in real-life scenarios. This paper presents an empirical review of different depth-matching techniques in real-life timelapse well logs, primarily focusing on gamma ray and the extent to which the outcomes of these techniques match the results from a human expert. Specifically, the performances of dynamic time warping (DTW), constrained DTW (CDTW), and correlation optimized warping (COW) are investigated. The experiments also consider the effects of filtering and normalization on the performance of each of the techniques. Concerning the correlations of each technique’s outcome with the reference data and an expert-generated outcome, this research identifies and discusses its key challenges, as well as provides recommendations for future research directions. Although the COW technique has its limitations, as discussed in this paper, our experiments demonstrate that it shows more potential than DTW and its variants in the well-log pattern alignment task. The work entailed by this research is significant because identifying and discussing the limitations of these techniques is vital for solution-oriented future research in this area.","PeriodicalId":170688,"journal":{"name":"Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131249866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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