Search Results (46)

The Qingshankou Formation shale in the Changling Sag of the Songliao Basin represents a typical lacustrine pure-shale reservoir, characterized by high organic matter abundance, high maturity, high clay mineral content, and strong heterogeneity. To elucidate the pore structure and heterogeneity of this shale, a comprehensive suite of analytical techniques—including X-ray diffraction (XRD), scanning electron microscopy (SEM), high-pressure mercury intrusion porosimetry (MICP), and low-temperature nitrogen adsorption—was employed to investigate its pore types and fractal characteristics systematically. On this basis, lithofacies classification and FHH fractal modeling were conducted to quantitatively assess the complexity of pore–throat structures and their influence on reservoir properties. The results indicate that shale-dominated lithofacies (Types A–C) exhibit higher surface fractal dimensions (D₁ = 2.51–2.58) and structural fractal dimensions (D₂ = 2.73–2.81), corresponding to low porosity, low permeability, and high displacement pressure. In contrast, carbonate- and clastic-dominated lithofacies (Types D–G) display lower fractal dimensions, suggesting more regular pore–throat structures and better connectivity. Overall, both D₁ and D₂ show negative correlations with porosity and permeability but positive correlations with displacement pressure, and are negatively correlated with TOC content, reflecting the intrinsic coupling among pore–throat complexity, reservoir capacity, and organic matter abundance. These findings reveal that the Qingshankou shale reservoir has undergone a geometric evolutionary pathway of “shale compaction → siltstone transition → carbonate porosity reconstruction.” The fractal dimensions effectively characterize the reservoir heterogeneity and pore–throat connectivity, providing a new theoretical basis for the quantitative characterization, classification, and potential prediction of continental shale oil reservoirs. Full article

Pore systems in continental shales are controlled by lithofacies and show strong heterogeneity, which challenges shale oil development. The Qingshankou Formation in the Songliao Basin is a major shale oil play in China. Previous studies have focused on macroscopic reservoir properties, with limited analysis of pore differences among lithofacies. This study integrates mineralogy, organic geochemistry, and multi-scale pore structure characterization to examine four typical lithofacies: argillaceous, siliceous, calcareous, and mixed shales. Results show that pore evolution in the Qingshankou Formation can be divided into five stages: immature (Ro < 0.6%), low maturity (0.6% < Ro ≤ 0.8%), middle maturity (0.8% < Ro ≤ 1.0%), high maturity (1.0% < Ro ≤ 1.2%), and over maturity (Ro > 1.2%). The overall pattern follows a “three declines and two increases” trend. Due to differences in mineral composition and organic matter (OM), each lithofacies displays dis-tinct pore characteristics, which further influence oil-bearing potential and mobility. Siliceous shale, rich in felsic minerals, exhibits well-preserved pores and a developed micro-fracture network, providing the largest pore volume and average diameter. This facilitates the storage and flow of free oil, making it the preferred exploration target. Argillaceous shale, characterized by abundant clay minerals and OM, supports micropore development and offers the highest specific surface area (SSA). This yields significant adsorbed oil potential, highlighting its value as a secondary exploration target. This study clarifies the lithofacial controls on pore development in continental shales, providing a scientific basis for predicting favorable intervals and optimizing exploration strategies in the Qingshankou Formation and analogous basins. Full article

Since the onset of the Cenozoic, the South China Sea has experienced complex plate interactions including peripheral plate collisions, the demise of the Paleo-South China Sea, and the subsequent opening of the modern basin. These processes produced three major types of sedimentary basins: extensional, strike-slip, and compressional. The Beikang Basin represents a typical extensional continental-margin rift basin that preserves the stratigraphic and sedimentary record of the transition from syn-rift to post-rift stages. Subsidence happened mainly during the post-rift stage. Five structural styles exist: extensional, compressional-inversion, strike-slip–extensional, magmatic, and diapiric. While the first three are fault-related, the last two are mainly controlled by the volcanic phases. Using a seismic-facies-to-sedimentary-system workflow, we delineate a tectono-stratigraphic framework, comprising five seismic facies, seven lithofacies, and eight depositional facies. This framework indicates that the Beikang Basin evolved through four major tectonic stages including initial rifting, inherited rifting, climax rifting, and post-rift thermal subsidence. Each stage has primary control on sediment supply and accommodation development. Our findings refine the basin’s tectono-sedimentary evolution and improve predictions for sediment distribution and hydrocarbon exploration in the underexplored Beikang Basin. Full article

Conglomerate reservoirs present significant technical challenges during drilling operations due to their complex mineral composition and heterogeneous characteristics, yet the quantitative relationships between mineral composition and microscopic mechanical behavior remain poorly understood. To elucidate the variation patterns of conglomerate micromechanical properties and their mineralogical control mechanisms, this study develops a novel multi-scale characterization methodology. This approach uniquely couples nanoindentation technology, micro-zone X-ray diffraction analysis, and machine learning algorithms to systematically investigate micromechanical properties of conglomerate samples from different regions. Hierarchical clustering algorithms successfully classified conglomerate micro-regions into three lithofacies categories with distinct mechanical differences: hard (elastic modulus: 81.90 GPa, hardness: 7.83 GPa), medium-hard (elastic modulus: 54.97 GPa, hardness: 3.87 GPa), and soft lithofacies (elastic modulus: 25.21 GPa, hardness: 1.15 GPa). Correlation analysis reveals that quartz (SiO₂) content shows significant positive correlation with elastic modulus (r = 0.52) and hardness (r = 0.51), while clay minerals (r = −0.37) and plagioclase content (r = −0.48) exhibit negative correlations with elastic modulus. Mineral phase spatial distribution patterns control the heterogeneous characteristics of conglomerate micromechanical properties. Additionally, a random forest regression model successfully predicts mineral content based on hardness and elastic modulus measurements with high accuracy. These findings bridge the gap between microscopic mineral properties and macroscopic drilling performance, enabling real-time formation strength assessment and providing scientific foundation for optimizing drilling strategies in heterogeneous conglomerate formations. Full article

The Lower Congo Basin (LCB) and the Niger Delta Basin (NDB), two end-member deep-water systems along the West African passive margin, exhibit contrasting sedimentary architectures despite shared geodynamic settings. The research comprehensively utilizes seismic reflection structure, root mean square amplitude slices, drilling lithology, changes in logging curves, and previous research achievements to elucidate the controlling mechanisms behind these differences. Key findings include: (1) Stark depositional contrast: Since the Eocene, the LCB developed retrogradational narrow-shelf systems dominated by erosional channels and terminal lobes, whereas the NDB formed progradational broad-shelf complexes with fan lobes and delta-fed turbidites. (2) Primary controls: Diapir-driven topographic features and basement uplift govern architectural variability, whereas shelf-slope break configuration and oceanic relief constitute subordinate controls. (3) Novel mechanism: First quantification of how diapir-induced seafloor relief redirects sediment pathways and amplifies facies heterogeneity. These insights establish a tectono-sedimentary framework for predicting deep-water reservoirs in diapir-affected passive margins, refine the conventional “source-to-sink” model by emphasizing salt-geomorphic features coupling as the primary driver. By analyzing the differences in lithofacies assemblages and sedimentary configurations among the above-mentioned different basins, this study can provide beneficial insights for the research on related deep-water turbidity current systems and also offer guidance for deep-water oil and gas exploration and development in the West African region and other similar areas. Full article

Lithology identification is one of the most important research areas in petroleum engineering, including reservoir characterization, formation evaluation, and reservoir modeling. Due to the complex structural environment, diverse lithofacies types, and differences in logging data and core data recording standards, there is significant overlap in the logging responses between different lithologies in the second member of the Lucaogou Formation in the Santanghu Basin. Machine learning methods have demonstrated powerful nonlinear capabilities that have a strong advantage in addressing complex nonlinear relationships between data. In this paper, based on felsic content, the lithologies in the study area are classified into four categories from high to low: tuff, dolomitic tuff, tuffaceous dolomite, and dolomite. We also study select logging attributes that are sensitive to lithology, such as natural gamma, acoustic travel time, neutron, and compensated density. Using machine learning methods, XGBoost, random forest, and support vector regression were selected to conduct lithology identification and favorable reservoir prediction in the study. The prediction results show that when trained with 80% of the predictors, the prediction performance of all three models has improved to varying degrees. Among them, Random Forest performed best in predicting felsic content, with an MAE of 0.11, an MSE of 0.020, an RMSE of 0.14, and a R² of 0.43. XGBoost ranked second, with an MAE of 0.12, an MSE of 0.022, an RMSE of 0.15, and an R² of 0.42. SVR performed the poorest. By comparing the actual core data with the predicted data, it was found that the results are relatively close to the XRD results, indicating that the prediction accuracy is high. Full article

Amid increasing interest in enhanced oil recovery and carbon geological sequestration programs, improved static reservoir lithofacies models are emerging as a requirement for well-guided project management. Building reservoir models can leverage seismic attribute clustering for seismic facies mapping. One challenge is that machine learning (ML) seismic facies mapping is prone to a wide range of equally possible outcomes when traditional unsupervised ML classification is used. There is a need to constrain ML seismic facies outcomes to limit the predicted seismic facies to those that meet the requirements of geological plausibility for a given depositional setting. To this end, this study utilizes an unsupervised comparative hierarchical and K-means ML classification of the whole 3D seismic data spectrum and a suite of spectral bands to overcome the cluster “facies” number uncertainty in ML data partition algorithms. This comparative ML, which was leveraged with seismic resolution data preconditioning, predicted geologically plausible seismic facies, i.e., seismic facies with spatial continuity, consistent morphology across seismic bands, and two ML algorithms. Furthermore, the variation of seismic facies classes was validated against observed lithofacies at well locations for the Mississippian carbonates of Kansas. The study provides a benchmark for both unsupervised ML seismic facies clustering and an understanding of seismic facies implications for reservoir/saline-aquifer aspects in building reliable static reservoir models. Three-dimensional seismic reflection P-wave data and a suite of well logs and drilling reports constitute the data for predicting seismic facies based on seismic attribute input to hierarchical analysis and K-means clustering models. The results of seismic facies, six facies clusters, are analyzed in integration with the target-interval mineralogy and reservoir porosity. The study unravels the nature of the seismic (litho) facies interplay with porosity and sheds light on interpreting unsupervised machine learning facies in tandem with both reservoir porosity and estimated (Umaa-RHOmaa) mineralogy. Full article

The lithofacies of a reservoir contain key information such as rock lithology, sedimentary structures, and mineral composition. Accurate prediction of shale reservoir lithofacies is crucial for identifying sweet spots for oil and gas development. However, obtaining shale lithofacies through core sampling during drilling is challenging, and the accuracy of traditional logging curve intersection methods is insufficient. To efficiently and accurately predict shale lithofacies, this study proposes a hybrid model called Stacking, which combines four classifiers: Random Forest, HistGradient Boosting, Extreme Gradient Boosting, and Categorical Boosting. The model employs the Grid Search Method to automatically search for optimal hyperparameters, using the four classifiers as base learners. The predictions from these base learners are then used as new features, and a Logistic Regression model serves as the final meta-classifier for prediction. A total of 3323 data points were collected from six wells to train and test the model, with the final performance evaluated on two blind wells that were not involved in the training process. The results indicate that the stacking model accurately predicts shale lithofacies, achieving an Accuracy, Recall, Precision, and F1 Score of 0.9587, 0.959, 0.9587, and 0.9587, respectively, on the training set. This achievement provides technical support for reservoir evaluation and sweet spot prediction in oil and gas exploration. Full article

The photoelectric Factor (PEF) log is a powerful tool for distinguishing between siliciclastic and carbonate lithofacies in well-log analysis and 2D correlations. However, its application in complex reservoirs has some challenges due to well spacing. We present a workflow to extend its capabilities into a 3D environment to characterize the Pennsylvanian Strawn and Canyon reef complex in the Salt Creek field, Kent County, West Texas. The productive zones within this reservoir are composed of porous oolitic grainstones and skeletal packstones. However, there are some porous shale beds within the reef complex that are indistinguishable from the porous limestone zones on the neutron porosity log that have posed major challenges to hydrocarbon production. To address these problems, we used a machine-learning procedure involving multiattribute analysis and probabilistic neural network (PNN) to predict photoelectric factor (PEF) volume to characterize the reservoir and identify the shale beds. By combining neutron porosity, gamma ray, and the predicted PEF logs, we found that (1) these shale beds, hereby referred to as shale-influenced carbonates, are characterized by photoelectric factor values ranging from 4 to 4.26 B/E. (2) Based on the PEF values, the least porous interval is the Canyon System, having <1% porosity and characterized by PEF values of >4.78 B/E; while the most porous interval is the Strawn System, composed mostly of zones with porosity ranging from 3% to 28%, characterized by PEF values varying from 4.26 to 4.78 B/E. Full article

Tight oil reservoirs are considered important exploration targets in lacustrine basins. High-quality reservoir prediction is difficult as the reservoirs have complex distributions of depositional facies and diagenesis processes. Previous research has found that the diagenesis process of tight oil sandstones varies greatly in different depositional facies. However, diagenesis variation in different depositional facies is still poorly studied, especially in distributary channels of shallow water delta deposits in lacustrine basins. Based on the description of core samples, the observation of rock slices, the interpretation of well logging data, and the analysis of porosity and permeability data, the differences in the lithofacies types, diagenesis processes, and pore structures of different distributary channels have been clarified. Ultimately, a model of diagenesis and reservoir heterogeneity distribution in the shallow-water delta of Chang 8 Member of the Yanchang Formation in the Caijiamiao area of the Ordos Basin has been established. This research indicates that the main distributary channels in the study area are dominated by massive bedding sandstone lithofacies, while the secondary distributary channels are primarily characterized by cross-bedding sandstone lithofacies. There are significant differences in the compaction, dissolution, and cementation of authigenic chlorite and carbonate among different parts of the distributary channels. Plastic mineral components, such as clay and mica, are abundant in sheet sands, and are more influenced by mechanical and chemical compaction. Influenced by the infiltration of meteoric water and hydrocarbon generation, dissolution pores are relatively well-developed in the underwater distributary channel reservoirs. A large amount of carbonate cementation, such as calcite and siderite, is found within the sandstone at the interface between sand and mud. The occurrence of authigenic chlorite exhibits a clear sedimentary microfacies zonation, but there is little difference in the kaolinite and siliceous cementation among different microfacies reservoirs. Finally, a model of diagenetic differences and reservoir quality distribution within dense sand bodies has been established. This model suggests that high-quality reservoirs are primarily developed in the middle of distributary channels, providing a theoretical basis for the further fine exploration and development of oil and gas in the study area. Full article

In order to enhance the development efficiency of thick and complex carbonate reservoirs in the Middle East, a case study was conducted on M oilfield in Iraq. This study focused on reservoir characterization, injection-production modes, well pattern optimization, and other related topics. As a result, key techniques for the high-efficiency development of thick carbonate reservoirs were established. The research findings include the following: (1) the discovery of hidden “low-velocity” features within the thick gypsum-salt layer, which led to the development of a new seismic velocity model; (2) the differential dissolution of grain-supported limestones is controlled by lithofacies and petrophysical properties, resulting in the occurrence of “porphyritic” phenomena in core sections. The genetic mechanism responsible for reversing petrophysical properties in dolostones is attributed to “big hole filling and small hole preservation” caused by dense brine refluxing; (3) fracture evaluation technology based on anisotropy and dipole shear wave long-distance imaging was developed to address challenges associated with quantitatively assessing micro-fractures; (4) through large-scale three-dimensional physical models and numerical simulations, it was revealed that water–oil displacement mechanisms involving “horizontal breakthrough via hyper-permeability” combined with vertical differentiation due to gravity occur in thick and heterogeneous reservoirs under spatial injection-production modes; (5) a relationship model linking economic profit with well pattern density was established for technical service contracts in the Middle East. Additionally, an innovative stepwise conversion composite well patterns approach was introduced for thick reservoirs to meet production ramp-up requirements while delaying water cut rise; (6) a prediction technology for the oilfield development index, considering asphaltene precipitation, has been successfully developed. These research findings provide robust support for the efficient development of the M oilfield in Iraq, while also serving as a valuable reference for similar reservoirs’ development in the Middle East. Full article

This paper utilizes prevalent deep learning techniques, such as Convolutional Neural Networks (CNNs) and Residual Neural Networks (ResNets), along with the well-established machine learning technique, Random Forest, to efficiently distinguish between common lithologies including coal, sandstone, limestone, and others. This approach is highly significant for resource extraction—such as coal, oil, natural gas, and groundwater—by streamlining the process and minimizing the need for the time-consuming manual interpretation of geophysical logging data. The natural gamma ray, density, and resistivity log data were collected from 22 wells in the mountainous region of Southern Sichuan, China, yielding approximately 70,000 samples for developing lithofacies prediction models. All the models achieved around 80% accuracy in classifying carbonaceous lithologies and up to 88% accuracy in predicting other lithologies. The trained models were applied to the logging data in the validation dataset, and the outputs were validated against geological core data, showing overall consistency, although variations in the classification results were observed across different wells. These findings suggest that deep learning techniques have the potential to develop a general model for effectively handling lithology classification with well logging data. Full article

Lacustrine fine-grained sedimentary rocks in the Dongying Sag of the Bohai Bay Basin in China exhibit significant potential for hydrocarbon exploration. This study investigates the lithofacies types and sedimentary evolution of the Paleogene Shahejie Formation’s lower third member (Es3l) and upper fourth member (Es4u), integrating petrological and geochemical analyses to explore the relationship between lithofacies characteristics and sedimentary environments. The results show that the fine-grained sedimentary rocks in the study area can be classified into 18 lithofacies, with seven principal ones, including organic-rich laminated carbonate fine-grained mixed sedimentary rock lithofacies and organic-rich laminated limestone lithofacies. In conjunction with analyses of vertical changes in geochemical proxies such as paleoclimate (e.g., CIA, Na/Al), paleoproductivity (e.g., Ba), paleosalinity (e.g., Sr/Ba), paleo-redox conditions (e.g., V/Sc, V/V + Ni), and terrigenous detrital influx (e.g., Al, Ti), five stages are delineated from bottom to top. These stages demonstrate a general transition from an arid to humid paleoclimate, a steady increase in paleoproductivity, a gradual decrease in paleosalinity, an overall reducing water body environment, and an increasing trend of terrestrial detrital input. This study demonstrates that the abundance of organic matter is primarily influenced by paleoproductivity and paleo-redox conditions. The variations in rock components are predominantly influenced by paleoclimate, and sedimentary structures are affected by the depth of the lake basin. Special depositional events, such as storm events in Stage II, have significantly impacted the abundance of organic matter, rock components, and sedimentary structures by disturbing the water column and disrupting the reducing conditions at the lake bottom. The present study offers crucial insights into the genesis mechanisms of continental lacustrine fine-grained sedimentary rocks, facilitates the prediction of lithofacies distribution, and advances the exploration of China’s shale oil resources in lacustrine environments. Full article

For the Jiawula lead–zinc deposit, as easily accessible resources become depleted, mines are becoming deeper to replenish ore reserves. Identifying large, continuous, and high-grade ore bodies in deep areas has become a daunting problem. Moreover, separating lead–zinc-bearing complex ore bodies from waste material and extracting them from associated minerals are also difficult. Thus, pioneering exploratory strategies and technological methodologies are required to make breakthroughs in mineral discovery. Based on extensive-scale structural lithofacies mapping, this paper uses short-wave infrared (SWIR) spectroscopy technology to investigate hydrothermal alteration minerals in the mining area. It has identified a total of 16 hydroxyl-bearing alteration minerals, including chlorite, muscovite, illite, calcite, ankerite, kaolinite, and smectite. These minerals establish zoning characteristics around the ore bodies and on their flanks. They comprise a segmented assemblage that follows the pattern of comb-textured quartz–illite–chlorite–carbonate → muscovite–illite–chlorite–ankerite → illite–smectite–chlorite → chlorite–kaolinite–calcite. Deep-zone illitization with a lower Al–OH absorbance peak wavelength (<2206 nm) and higher crystallinity indices (>1.1) and chloritization with higher Fe–OH absorbance peak wavelengths (>2254) and higher crystallinity indices (>3.0) are indicators of potential hydrothermal centers in the deeper regions. By finding hydrothermal centers and connecting their spatial distribution with existing ore bodies, a pertinent relationship between diabase + andesite, Fe-chlorite + illite, and high-grade mineralization has been established. They correspond well with the lithology-alteration mineralization. This research provides a basis for predicting the positioning of concealed ore bodies deep inside a mine or at the periphery. Full article

The diagenetic facies of tight oil reservoirs reflect the diagenetic characteristics and micro-pore structure of reservoirs, determining the formation and distribution of sweet spot zones. By establishing the correlation between diagenetic facies and logging curves, we can effectively identify the vertical variation of diagenetic facies types and predict the spatial variation of reservoir quality. However, it is still challenging work to establish the correlation between logging and diagenetic facies, and there are some problems such as low accuracy, high time consumption and high cost. To this end, we propose a lithofacies identification method for tight oil reservoirs based on hybrid intelligence using the Fuyu oil layer of the Sanzhao depression in Songliao Basin as the target area. Firstly, the geological characteristics of the selected area were analyzed, the definition and classification scheme of diagenetic facies and the dominant diagenetic facies were discussed, and the logging response characteristics of various diagenetic facies were summarized. Secondly, based on the standardization of logging curves, the logging image data set of various diagenetic facies was built, and the imbalanced data set processing was performed. Thirdly, by integrating CNN (Convolutional Neural Networks) and ViT (Visual Transformer), the C-ViTM hybrid intelligent model was constructed to identify the diagenetic facies of tight oil reservoirs. Finally, the effectiveness of the method is demonstrated through experiments with different thicknesses, accuracy and single-well identification. The experimental results show that the C-ViTM method has the best identification effect at the sample thickness of 0.5 m, with Precision of above 86%, Recall of above 90% and F1 score of above 89%. The calculation result of the Jaccard index in the identification of a single well was 0.79, and the diagenetic facies of tight reservoirs can be identified efficiently and accurately. At the same time, it also provides a new idea for the identification of the diagenetic facies of old oilfields with only logging image data sets. Full article