Facies analysis and distribution of Late Palaeogene deep‐water massive sandstones in submarine‐fan lobes, <scp>NW</scp> Borneo

Jamil, Muhammad; Siddiqui, Numair Ahmed; Usman, Muhammad; Wahid, Ali; Umar, Muhammad; Ahmed, Nisar; Haq, Izhar Ul; El‐Ghali, Mohamed A.K.; Imran, Qazi Sohail; Rahman, Abdul Hadi Abd; Zhang, Shiqi

doi:10.1002/gj.4553

Cited by 11 publications

(6 citation statements)

References 89 publications

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“…Too large or too small a depth will lead to overfitting and reduce the accuracy of the model. (3) The number of features during tree splitting determines correlation of trees, and the model is accurate with a low correlation of trees. Combine GS and KVC to select the best parameters improve the accuracy of the RF model.…”

Section: Gs-kcv-rf For Lithofacies Identificationmentioning

confidence: 99%

“…Lithofacies are inherent properties of rocks or rock combinations formed under certain sedimentary conditions that reflect specific processes or environments. Lithofacies can reflect the sedimentary environment for facies analysis, and determine the fundamental properties of subsurface reservoirs [1][2][3][4]. Determining the lithofacies type and distribution is critical for shale oil exploration and development [5,6].…”

Section: Introductionmentioning

confidence: 99%

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Data-Driven Classification and Logging Prediction of Mudrock Lithofacies Using Machine Learning: Shale Oil Reservoirs in the Eocene Shahejie Formation, Bonan Sag, Bohai Bay Basin, Eastern China

Chang,

Ruan,

et al. 2024

Minerals

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As the world’s energy demand continues to expand, shale oil has a substantial influence on the global energy reserves. The third submember of the Mbr 3 of the Shahejie Fm, characterized by complicated mudrock lithofacies, is one of the significant shale oil enrichment intervals of the Bohai Bay Basin. The classification and identification of lithofacies are key to shale oil exploration and development. However, the efficiency and reliability of lithofacies identification results can be compromised by qualitative classification resulting from an incomplete workflow. To address this issue, a comprehensive technical workflow for mudrock lithofacies classification and logging prediction was designed based on machine learning. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were conducted to realize the automatic classification of lithofacies, which can classify according to the internal relationship of the data without the disturbance of human factors and provide an accurate lithofacies result in a much shorter time. The PCA and HCA results showed that the third submember can be split into five lithofacies: massive argillaceous limestone lithofacies (MAL), laminated calcareous claystone lithofacies (LCC), intermittent lamellar argillaceous limestone lithofacies (ILAL), continuous lamellar argillaceous limestone lithofacies (CLAL), and laminated mixed shale lithofacies (LMS). Then, random forest (RF) was performed to establish the identification model for each of the lithofacies and the obtained model is optimized by grid search (GS) and K-fold cross validation (KCV), which could then be used to predict the lithofacies of the non-coring section, and the three validation methods showed that the accuracy of the GS–KCV–RF model were all above 93%. It is possible to further enhance the performance of the models by resampling, incorporating domain knowledge, and utilizing the mechanism of attention. Our method solves the problems of the subjective and time-consuming manual interpretation of lithofacies classification and the insufficient generalization ability of machine-learning methods in the previous works on lithofacies prediction research, and the accuracy of the model for mudrocks lithofacies prediction is also greatly improved. The lithofacies machine-learning workflow introduced in this study has the potential to be applied in the Bohai Bay Basin and comparable reservoirs to enhance exploration efficiency and reduce economic costs.

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Section: Gs-kcv-rf For Lithofacies Identificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Data-Driven Classification and Logging Prediction of Mudrock Lithofacies Using Machine Learning: Shale Oil Reservoirs in the Eocene Shahejie Formation, Bonan Sag, Bohai Bay Basin, Eastern China

Chang,

Ruan,

et al. 2024

Minerals

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“…A submarine fan is defined as a kind of fan-shaped or conical sedimentary body, which transports terrigenous debris to the seafloor through the submarine canyon in the front of the continental slope [10][11][12]. The submarine-fan sandstone reservoirs deposited in a deep water environment has strong heterogeneity owing to variable depositional textures and compositions among different lithofacies [13,14].…”

Section: Introductionmentioning

confidence: 99%

Diagenetic Impact on High-Pressure High-Temperature Reservoirs in Deep-Water Submarine Fan Sandstone of Qiongdongnan Basin, South China Sea

Hu,

Luo,

2024

Minerals

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The diagenetic evolution of sandstone is very complicated under the conditions of high temperatures and pressures in deep-water, deep-buried regimes, which have great influence on reservoir quality. This study investigates the typical reservoir target of Neogene deep-water, submarine-fan sandstones under high-temperature, high-pressure regimes in the Qiongdongnan Basin, South China Sea. Utilizing a thin section, scanning electron microscope (SEM), mineral geochemistry combined with burial history evolution, complex diagenetic events, and main controlling factors of the sandstone in the Neogene Meishan Formation were determined. The results show that the evolution of sandstone reservoirs is initially controlled by depositional framework compositions and subsequently modified by eogenetic and mesogenetic alterations during progressive burial. Eogenetic alterations mainly include the following: (1) mechanical compaction; (2) dissolution of feldspar; (3) low-Fe calcite cementation. Mesogenetic events were identified as the following: (1) dissolution of feldspar; (2) ferroan calcite and ankerite formation; (3) precipitation of quartz and clay mineral. Mechanical compaction is greatly influenced by the original depositional framework composition, and sandstone samples enriched in high contents of detrital clay matrix always experienced extensive mechanical compaction. Different phases of carbonate cement during different diagenetic regimes lead to continuous destruction on reservoir porosity. The dissolution of unstable feldspar minerals during eogenetic and mesogenetic environments leads to the development of secondary porosities and would enhance the quality of the reservoir. Overpressure formation is pervasively developed owing to early disequilibrium compaction and subsequent natural gas charging. Only well-sorted sandstones with low contents of detrital clay matrix could resist early mechanical compaction, lead to ample residual original porosities, and then undergo extensive mineral dissolution to generate sufficient secondary porosities. Subsequently, these porosities would be effectively protected by overpressure formation. Poor-sorted sandstones with high contents of detrital clay matrix would experience strong mechanical compaction and extensive destruction of original porosities. Thus, these sandstones are difficult to have significant dissolution and are unable to be effectively protected by overpressure formation. Therefore, the interplay between the original framework composition and the corresponding diagenetic pathways coupled with overpressure formation would result in strong reservoir heterogeneity for the deep-buried sandstones during progressive burial.

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“…These reservoirs exhibit poor physical Tight sandstone reservoirs in the Ordos Basin exist mainly in the lower assemblage, especially in the Chang 7-Chang 10 section [14]. These reservoirs exhibit poor physical properties, characterized by complex pore throat structure, pronounced heterogeneity, and complicated rock-electric relationships [15,16]. There are the following difficulties in logging interpretation: the interpretation model based on the relationship study of the "four properties" (lithological characteristics, physical characteristics, electrical characteristics and oil-bearing characteristics) of the conventional reservoir cannot effectively meet the evaluation of logging in tight sandstone reservoirs [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Modeling of Quantitative Characterization Parameters and Identification of Fluid Properties in Tight Sandstone Reservoirs of the Ordos Basin

Xu,

Wang,

Song

et al. 2024

Processes

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The Ordos Basin has abundant resources in its tight sandstone reservoirs, and the use of well logging technology stands out as a critical element in the exploration and development of these reservoirs. Unlike conventional reservoirs, the commonly used interpretation models are not ideal for evaluating tight sandstone reservoirs through logging. In order to accurately evaluate parameters and identify fluid properties in the tight sandstone reservoirs of the Ordos Basin, we propose the adaption of conventional logging curves. This involves establishing an interpretation model that integrates the response characteristics of logging curves to tight sandstone reservoirs in accordance with the principles of logging. In this approach, we create interpretation models specifically for shale content, porosity, permeability, and saturation within the tight sandstone reservoir. Using the characteristics of the logging curves and their responses, we apply a mathematical relationship to link these parameters and create a template for identifying fluid properties within tight sandstone reservoirs. The average absolute errors of the new multi-parameter weighting method shale content interpretation model and porosity classification saturation interpretation model for quantitative evaluation of reservoir shale content and oil saturation are small, and the accuracy meets the production requirements. In this paper, the four-step method is used to identify the fluid properties of tight sandstone reservoirs step by step, which is to use the interrelationship between curves, eliminate the useless information, enhance the useful information, and finally solve the problem of identifying the fluid properties of tight sandstone reservoirs, which is difficult to identify, and realize the linear discrimination of the interpretation standard, which improves the accuracy of interpretation. The proven multi-information, four-step, step-by-step fluid property identification template has an accuracy of more than 90%. The interpretation model has been applied to 20 wells on the block with a compliance rate of 95.23%, providing the basis for accurately establishing the tight sandstone interpretation standard. The newly introduced log evaluation approach for tight sandstone reservoirs effectively overcomes the technical hurdles that have previously hindered the evaluation of such reservoirs in the Ordos Basin. This method is suitable for wide application and can be used for quantitative evaluation of tight sandstone reservoirs in different regions.

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Facies analysis and distribution of Late Palaeogene deep‐water massive sandstones in submarine‐fan lobes, NW Borneo

Cited by 11 publications

References 89 publications

Data-Driven Classification and Logging Prediction of Mudrock Lithofacies Using Machine Learning: Shale Oil Reservoirs in the Eocene Shahejie Formation, Bonan Sag, Bohai Bay Basin, Eastern China

Data-Driven Classification and Logging Prediction of Mudrock Lithofacies Using Machine Learning: Shale Oil Reservoirs in the Eocene Shahejie Formation, Bonan Sag, Bohai Bay Basin, Eastern China

Diagenetic Impact on High-Pressure High-Temperature Reservoirs in Deep-Water Submarine Fan Sandstone of Qiongdongnan Basin, South China Sea

Modeling of Quantitative Characterization Parameters and Identification of Fluid Properties in Tight Sandstone Reservoirs of the Ordos Basin

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