Reservoir Lithology Research Articles

Developing a coupled geo-hydrostratigraphic model for a complex lithologic reservoir: a case study of Dakhla Basin, Southwestern Morocco

Developing a coupled geo-hydrostratigraphic model for a complex lithologic reservoir: a case study of Dakhla Basin, Southwestern Morocco

Characteristics and hydrocarbon accumulation model of Paleogene whole petroleum system in western depression of Qaidam Basin, NW China

Based on the oil and gas exploration in western depression of the Qaidam Basin, NW China, combined with the geochemical, seismic, logging and drilling data, the basic geological conditions, oil and gas distribution characteristics, reservoir-forming dynamics, and hydrocarbon accumulation model of the Paleogene whole petroleum system (WPS) in the western depression of the Qaidam Basin are systematically studied. A globally unique ultra-thick mountain-style WPS is found in the western depression of the Qaidam Basin. Around the source rocks of the upper member of the Paleogene Lower Ganchaigou Formation, the structural reservoir, lithological reservoir, shale oil and shale gas are laterally distributed in an orderly manner and vertically overlapped from the edge to the central part of the lake basin. The Paleogene WPS in the western depression of the Qaidam Basin is believed unique in three aspects. First, the source rocks with low organic matter abundance are characterized by low carbon and rich hydrogen, showing a strong hydrocarbon generating capacity per unit mass of organic carbon. Second, the saline lake basinal deposits are ultra-thick, with mixed deposits dominating the center of the depression, and strong vertical and lateral heterogeneity of lithofacies and storage spaces. Third, the strong transformation induced by strike-slip compression during the Himalayan resulted in the heterogeneous enrichment of oil and gas in the mountain-style WPS. As a result of the coordinated evolution of source-reservoir-caprock assemblage and conducting system, the Paleogene WPS has the characteristics of “whole process” hydrocarbon generation of source rocks which are low-carbon and hydrogen-rich, “whole depression” ultra-thick reservoir sedimentation, “all direction” hydrocarbon adjustment by strike-slip compressional fault, and “whole succession” distribution of conventional and unconventional oil and gas. Due to the severe Himalayan tectonic movement, the western depression of the Qaidam Basin evolved from depression to uplift. Shale oil is widely distributed in the central lacustrine basin. In the sedimentary system deeper than 2 000 m, oil and gas are continuous in the laminated limy-dolomites within the source rocks and the alga limestones neighboring the source kitchen, with intercrystalline pores, lamina fractures in dolomites and fault-dissolution bodies serving as the effective storage space. All these findings are helpful to supplement and expand the WPS theory in the continental lake basins in China, and provide theoretical guidance and technical support for oil and gas exploration in the Qaidam Basin.

Accumulation sequence and exploration domain of continental whole petroleum system in Sichuan Basin, SW China

Based on the oil and gas exploration in the Sichuan Basin, combined with data such as seismic, logging and geochemistry, the basic geological conditions, hydrocarbon types, hydrocarbon distribution characteristics, source- reservoir relationship and accumulation model of the Upper Triassic–Jurassic continental whole petroleum system in the basin are systematically analyzed. The continental whole petroleum system in the Sichuan Basin develops multiple sets of gas-bearing strata, forming a whole petroleum system centered on the Triassic Xujiahe Formation source rocks. The thick and high-quality source rocks in the Upper Triassic Xujiahe Formation provide sufficient gas source basis for the continental whole petroleum system in the basin. The development of conventional-unconventional reservoirs provides favorable space for hydrocarbon accumulation. The coupling of faults and sandbodies provides a high-quality transport system for gas migration. Source rocks and reservoirs are overlapped vertically, and there are obvious differences in sedimentary environment, reservoir lithology and physical properties, which lead to the orderly development of inner-source shale gas, near-source tight gas, and far-source tight–conventional gas in the Upper Triassic–Jurassic, from bottom to top. The orderly change of geological conditions such as burial depth, reservoir physical properties, formation pressure and hydrocarbon generation intensity in zones controlled the formation of the whole petroleum system consisting of structural gas reservoir in thrust zone, shale gas-tight gas reservoir in depression zone, tight gas reservoir in slope zone, and tight gas–conventional gas reservoir in uplift zone on the plane. Based on the theory and concept of the whole petroleum system, the continental shale gas and tight gas resources in the Sichuan Basin have great potential, especially in the central and western parts with abundant unconventional resources.

Pattern recognition approach (PRA) for identifying oil reservoir lithology of Camaal oil field, Yemen

The accurate determination of reservoir lithology remains a challenge in petroleum engineering. There are some conventional techniques available to determine the lithology. However, the application of those techniques has been long and complex. So, the main goal of this study is to simplify the identification of reservoir lithology. This paper presents a Pattern Recognition Approach (PRA) to identify the reservoir lithology simply and accurately. It is type of artificial neural network. Four wells from the Camaal Field were chosen to develop this approach. Around 32400 data points from the previous wells were digitized. The PRA approach used depth, gamma ray, lithology, sonic, neutron, and density logs as inputs. The model classifies lithology into permeable and impermeable rocks, further categorizing them into clastic and carbonate rocks, and subsequently into specific types into sand, sandstone, dolomite and limestone. The results show that the proposed approach provides a suitable prediction of lithology with higher accuracy compared with actual lithology. The model demonstrates high accuracy rates in identifying various lithologies, with overall accuracies of 76.2% for permeable/impermeable rocks, 94.9 for clastic/carbonate rocks, 86.2% for sand/sandstone, and 92.8% for dolomite/limestone.

Prestack reservoir prediction method in ray parameter domain based on wide azimuth ocean bottom node seismic data

AbstractWide azimuth seismic data play an important role in deep reservoir prediction. According to the Paleogene clastic rock reservoir prediction, the high‐density and wide azimuth 3D seismic data acquisition of ocean bottom nodes was first carried out in a Chinese offshore oilfield in 2019. After high precision amplitude‐preserving processing, we obtained the high‐quality wide azimuth gathers. However, the research on anisotropy and reservoir prediction using wide azimuth seismic data mainly focuses on carbonate and bedrock intervals, which is not suitable for clastic rock reservoir prediction. Therefore, this paper innovatively proposes a clastic rock reservoir prediction method, which studies prestack reservoir prediction in the ray parameter domain based on wide azimuth ocean bottom node seismic data. Based on the azimuth gathers, we can obtain elastic parameters through prestack amplitude versus offset inversion, which is used to characterize the reservoir, so it is significant to obtain high precision elastic parameters in order to get highly reliable reservoir prediction results. In this paper, we develop an amplitude versus offset inversion method based on the Bayesian theory in ray parameter domain, the output of which is density, P‐wave impedance and Vp/Vs. These elastic parameters have high precision, and density data are valuable input for reservoir characterization because they are sensitive to the lithology of clastic rock reservoir at different orientations. In ray parameter domain inversion, the ray path of seismic wave propagation is considered polyline, which is more consistent with the actual situation; thus, extracted amplitudes of P gathers used in inversion are more accurate. In addition, the reflection coefficient formula in ray parameter domain has higher precision when the incident angle is large. The inversion based on the Bayesian theory can improve the stability of the inversion. Test on the actual data shows that the result of ray parameter domain inversion with a Bayesian scheme is more accurate, stable and reliable. Based on the above high precision density inversion results, an innovative wide azimuth data reservoir prediction technology based on elliptical short‐axis fitting was proposed. The actual prediction of the deep reservoir in the Bohai oilfield shows that sand thickness fitting prediction results in the short axis can best match the actual drilling sandstone thickness. The coincidence rate is 86% and the short‐axis fitting results are more in agreement with geological laws. Theoretical research and practical applications have shown that this method is feasible and effective, with high prediction accuracy, computational efficiency and strong application value.

Pore structure of the mixed sedimentary reservoir of Permian Fengcheng Formation in the Hashan area, Junggar Basin

The Permian Fengcheng Formation (P1f.) in the Hashan area, situated on the southwestern margin of the Junggar Basin, has witnessed a remarkable breakthrough in shale oil exploration in recent years with nearly 789 million tons of shale oil resources. As a unique set of mixed sedimentary shales, the Fengcheng Formation in the Hashan area is characterized by mixed sedimentation of terrigenous siliciclastic sediments, authigenic minerals, and tuffaceous materials. However, the understanding of pore characteristics in the mixed sedimentary reservoir still remains limited, prohibiting accurate estimation of the oil content and insights into oil mobility. Scanning electron microscopy (SEM), X-ray diffraction (XRD), mercury injection capillary pressure (MICP), nuclear magnetic resonance (NMR), X-Ray Computer Tomography (X-CT), and geochemical analysis were performed to investigate the pore size distribution and main controlling factors of the mixed sedimentary reservoir. Results showed that the main pore types in the mixed sedimentary reservoir are intergranular pores and dissolution pores. The pores of the P1f. mixed shales in the Hashan area were classified into II-micropores (< 25 nm), I-micropores (25–100 nm), mesopores (100–1000 nm) and macropores (> 1000 nm). In general, the mixed sedimentary rocks of P1f. formation feature few macropores but a large number of micropores and mesopores. The CS exhibits the most favourable physical properties among all lithofacies. It is concluded that the abundance and maturity of organic matter, mineral composition, sedimentary structure, and diagenesis of reservoir together impact the pore structure in the mixed sedimentary reservoirs. The maturity of organic matter and the content of tuffaceous minerals are the most significant in influencing the pore structure of P1f. shales. Overall, the pore structure of complex lithologic reservoir formed by mixed deposition and its influence on physical properties are studied, and the characteristics of the microscopic pore-throat system of the dominant lithofacies in the Hashan area are clarified, which is of great significance as a guide for the exploration and development of mixed sedimentary reservoirs in continental shale oil in China.

Improved pore structure characterization and classification of strong diagenesis sandstones by data-mining analytics in Tazhong area, Tarim Basin.

The Silurian system in Tazhong area is characterized by extensive, low-abundance lithological reservoirs with strong diagenesis, resulting in significant heterogeneity. The complex pore structure in this area significantly impacts fluid control, making accurate characterization and classification of pore structures crucial for understanding reservoir properties and their influence on oil and gas distribution. Based on 314 Mercury Injection Capillary Pressure (MICP) samples in combination with core slices and thin casting slices observation, a pipeline of characterization and classification scheme by data-mining analytics of strong diagenesis sandstone pore structure types in the study zone is established, and the characteristics of different pore structures are clarified. According to the pore structure parameter abstracted by MICP data compression and variable analysis based on hierarchical clustering and principal component analysis (PCA) analysis, the variables are reasonably evaluated and screened, and the screened variables can be divided into three groups: mean pore throat radius-maximum pore throat radius-median pore throat radius-pore throat diameter mean variable group, microscopic mean coefficient variable group, and median pressure displacement pressure-relative sorting coefficient variable group. The combination of classification schemes analysed by decision tree model and linear discriminant analysis (LDA) model was determined. In the two-dimensional projection diagram of LDA model, a relatively obvious distribution of low displacement pressure, middle displacement pressure and high displacement pressure was obtained, and three distribution lines were nearly parallel. Based on the relevant information, 6 combined classification schemes suitable for final pore structure modelling were determined verified by microscopic observation. The correct characterization and classification of pore structure can be applied to the prediction of pore type, which can be used to improve the prediction of oil and gas distribution and oil and gas recovery in the future.

Logging Identification Methods for Oil-Bearing Formations in the Chang 6 Tight Sandstone Reservoir in the Qingcheng Area, Ordos Basin

The Chang 6 sandstone reservoir of the Upper Triassic Yanchang Formation in the Ordos Basin is one of the tight-oil-rich intervals in the basin. Owing to the strong heterogeneity and complex lithology of the Chang 6 reservoir, lithology and fluid identification have become more challenging, hindering exploration and development. This study focused on the Chang 6 member in the Qingcheng area of the Ordos Basin to systematically analyze the lithology, physical properties, and oil-bearing properties of the Chang 6 reservoir. We adopted the method of normalized superposition of neutron and acoustic time-difference curves, the method of induced conductivity–porosity–density intersection analysis, the method of superposition of difference curves (Δφ), and the induced conductivity curve. Our results indicated that the method of normalized superposition of neutron and acoustic wave time-difference curves could quickly and effectively identify the lithologies of tight fine sandstone, silty mudstone, mudstone, and carbonaceous mudstone. The induced conductivity–porosity–density cross-plot could be used to effectively identify oil and water layers, wherein the conductivity of tight oil layers ranged from 18 to 28.1 mS/m, the density ranged from 2.42 to 2.56 g/cm3, the porosity was more than 9.5%, and the oil saturation was more than 65%. Based on the identification of tight fine sandstone using the dual-curve normalized superposition method, the oil layer thickness within the tight fine sandstone could be effectively identified using the superposition of difference curves (Δφ) and induced conductivity curves. Verified by oil-bearing reservoir data from the field test, the overall recognition accuracy of the plots exceeded 90%, effectively enabling the identification of reservoir lithology and fluid types and the determination of the actual thickness of oil layers. Our results provide a reference for predicting favorable areas in the study area and other tight reservoirs.

The hydraulic fracture propagation pattern induced by multi-stage temporary plugging and diverting fracturing in reservoirs with various lithologies: An experimental investigation

Multi-stage temporary plugging and diverting fracturing (TPDF) is an effective method for generating hydraulic fracture (HF) networks. This study investigates various lithological reservoirs in the Xinjiang region, obtaining downhole full-diameter cores for experimental analysis using true triaxial TPDF. The characteristics of HF morphology are quantitatively assessed by employing computed tomography (CT) scanning. The findings are summarized as follows: (1) Initial hydraulic fracturing of specimens with different lithologies results in σH-direction double-wing HF, while the first TPDF generates a single-wing HF along the σh direction, and the second TPDF produces a single-wing HF along the σH direction. (2) The volume and area of HFs in the first TPDF of volcanic rock increased by over 30%. The first TPDF effect is more pronounced in conglomerate rock, with HF volume over 25% and surface area increasing by more than 35%. (3) During multi-stage TPDF, volcanic rock transitions from initial HF to the formation of new HF, sandstone diverts from the wellbore to create new HF, and conglomerate generates new HF through multi-point initiation in the wellbore and HF. Each TPDF process involves the propagation of existing HFs and the generation of new ones. (4) The breakdown pressure in multi-stage TPDF increased by 46.5% and 51.6% in volcanic rock, while in sandstone, the first TPDF increased by 90.6%. In conglomerate rock, multi-stage TPDF saw increases of 51.2% and 41.9%, respectively. These findings offer theoretical insights for optimizing TPDF design in diverse lithological reservoirs.

Characteristics and Genesis of Carbonate Weathering Crust Reservoirs: A Case from the Ma5Member of Ordovician in Gaoqiao Area, Ordos Basin, China.

The hydrocarbon reserves in carbonate rocks account for about half of the global hydrocarbon reserves and are an important reservoir type. The Ordovician Majiagou Formation in the central Ordos Basin is a representative weathering crust reservoir. The Caledonian movement uplifted the stratum as a whole, and subsequently, 120 million years of exposed weathering, denudation, and leaching created this unique karst paleomorphology. Dolomite reservoirs have developed dissolved pores and microfractures, which are the best reserved spaces for natural gas and good hydrocarbon migration channels. This paper takes the Ma5Member (hereinafter referred to as Ma51+2) carbonate reservoir in Gaoqiao Gas Field as the research target, based on the core, thin section, cathodoluminescence, logging data, etc., and systematically study the effect of karstification on the reservoir and the genesis of the dolomite reservoir. The results show that the depositional period of the Ordovician Majiagou strata is a regression cycle and the depositional environment is a limited evaporative tidal flat. The reservoir lithology of Ma51+2 is mainly gypsiferous dolomite and micrite dolomite. The reservoir space types consist of intergranular pores, gypsum mold pore, intragranular dissolution pores, intercrystalline pores, and microfractures. The porosity has values from 0.3 to 11.2% (mostly less than 5.0%) with an average of 3.3%, and the permeability ranges from 0.003 to 13.2 mD (mostly less than 1 mD) with an average of 0.36 mD. Karstification is divided into three periods, including syngenetic karst, eogenetic karst, and burial karst. The sedimentary microfacies determine the material basis of the reservoir, and multistage karstification finally modifies the physical properties. By deeply exploring the formation mechanism and influencing factors of the carbonate reservoir in the Ordovician Majiagou Formation, it provides an important theoretical basis and practical guidance for oil and gas exploration and development. At the same time, it also has important reference value for understanding and predicting the development law and distribution characteristics of carbonate reservoirs under similar geological background.

Improved pore structure prediction based on a stacking machine learning model for low-permeability reservoir in Tazhong area, Tarim Basin

Stacking is an ensemble machine learning method designed to improve overall performance by combining the predictions of multiple base learners. The core idea of Stacking is to use the output of different base learners as input and make final predictions through a meta-learner, allowing the model to benefit from the strengths of various base learners and adapt better to complex data patterns. Predicting pore structures in low permeability is a sophisticated nonlinear modeling issue affected by multiple factors, including sedimentary and diagenetic effects on the structure. An individual model to predict pore structure is not satisfactory. In this paper, we applied a fusion of random forest and Adaboost models as base learners, which emerges as the cornerstone of this research, showcasing remarkable versatility and integration of diverse learner strengths. The Silurian system in Tazhong area features a vast expanse of lithological reservoirs with low abundance and high heterogeneity. Capillary pressure plays a crucial role in the distribution of these less permeable reservoirs by pore structure type influence. Aiming to predict the pore structure for the low-permeability reservoir in the field, which is characterized by low abundance and high heterogeneity, the study strategically integrates geological theory into specialized databases and harnesses machine learning methods. The resulting machine learning modeling dataset leverages geological factors and logging response characteristics to strengthen the ensemble machine learning stacking model. This sophisticated model not only achieves exceptional accuracy but also undergoes rigorous validation by analyzing predicted pore structure types alongside production data. The model's average accuracy was 79.4%, which practically hit the test set's accuracy threshold, meaning that the model was in an optimal state. In essence, the proficiency of the stacking model in predicting pore structure types serves as a transformative force, offering significant advancements in hydrocarbon distribution predictions and paving the way for more streamlined and effective future EOR activities.

Shear-wave velocity prediction of tight reservoirs based on poroelasticity theory: A comparative study of deep neural network and rock physics model

The absence of shear-wave (S-wave) velocity in well log data limits the effective seismic characterization of lithology, petrophysical properties, and fluid distribution of tight reservoirs in the Chang 7 shale oil formations of Ordos Basin, west China. However, conventional rock physics models (RPMs), e.g., the Xu-White model, struggle to accurately obtain the key parameters such as pore aspect ratio in the process of S-wave velocity prediction. This work performs a comparative study of S-wave velocity prediction based on deep neural network (DNN) and RPM for such reservoirs. By employing the poroelasticity equations which are capable of describing elastic wave propagation in the complex reservoirs saturated with viscous fluid, the relation between elastic and petrophysical properties is established with a DNN-based method. This approach quantifies the connection between P-/S-wave velocities and rock properties. On the other hand, we reformulate the traditional Xu-White model by incorporating the decoupled Kuster-Toksöz (K-T) model and the poroelasticity equations with viscoelastic fluid in the modeling process. The variation of pore aspect ratio with depth is also considered to characterize the complex pore structures, which is appropriate for improving the accuracy of prediction. Log data from the three wells are considered for verification, of which results show that the S-wave velocity prediction methods help improve the final result. Aided by the DNN-based method, the prediction exhibits a better performance if the training data is sufficient. In particular, provided a small amount of measured S-wave data (even ten data points), the trained DNN model can also provide reasonable predictions.

Multi-task learning for seismic elastic parameter inversion with the lateral constraint of angle-gather difference

Pre-stack seismic inversion is an effective way to investigate the characteristics of hydrocarbon-bearing reservoirs. Multi-parameter application is the key to identifying reservoir lithology and fluid in pre-stack inversion. However, multi-parameter inversion may bring coupling effects on the parameters and destabilize the inversion. In addition, the lateral recognition accuracy of geological structures receives great attention. To address these challenges, a multi-task learning network considering the angle-gather difference is proposed in this work. The deep learning network is usually assumed as a black box and it is unclear what it can learn. However, the introduction of angle-gather difference can force the deep learning network to focus on the lateral differences, thus improving the lateral accuracy of the prediction profile. The proposed deep learning network includes input and output blocks. First, angle gathers and the angle-gather difference are fed into two separate input blocks with ResNet architecture and Unet architecture, respectively. Then, three elastic parameters, including P- and S-wave velocities and density, are simultaneously predicted based on the idea of multi-task learning by using three separate output blocks with the same convolutional network layers. Experimental and field data tests demonstrate the effectiveness of the proposed method in improving the prediction accuracy of seismic elastic parameters.

Utilizing logs to identify complex lithology of tight marl reservoir in the Leikoupo Formation 32 Submember (T2l32) of the Sichuan Basin, China

Recent exploration has revealed that the Middle Triassic Leikoupo Formation 32 Submember (T2l32) in the Sichuan Basin contains unconventional marl reservoirs with significant natural gas potential. Due to limited cores, old wells, and conventional logs, however, the lithological understanding of T2l32 is incomplete and relies solely on inaccurate mud logs. This lack of lithological foundation challenges geology and petroleum research. To identify complex lithology, this paper presents a double-hierarchical workflow to identify seven types of lithology using logs. The first order distinguishes salt, anhydrite, and marl, while the second order further subdivides marl into anhydrite marl, argillaceous limestone, shaly limestone, and limy shale. Different rocks’ logging response characteristics are summarized based on quantity-limited cores and micro-resistivity imaging logs. Lithological identification of 2D and 3D plots is established using sensitive GR, DEN, and RT. Corresponding identification standards are built in two hierarchies. According to these standards, the lithology of T2l32 is identified in a total of 119 wells. Finally, the lithological characteristics of vertical, horizontal, and plane are discussed in the research area. The research results may aid in comprehending the entire lithological characteristics of the complex marl reservoir in T2l32 of the Sichuan Basin. It would help the exploration potential of petroleum systems in turn.

A Transformer and LSTM-Based Approach for Blind Well Lithology Prediction

Petrographic prediction is crucial in identifying target areas and understanding reservoir lithology in oil and gas exploration. Traditional logging methods often rely on manual interpretation and experiential judgment, which can introduce subjectivity and constraints due to data quality and geological variability. To enhance the precision and efficacy of lithology prediction, this study employed a Savitzky–Golay filter with a symmetric window for anomaly data processing, coupled with a residual temporal convolutional network (ResTCN) model tasked with completing missing logging data segments. A comparative analysis against the support vector regression and random forest regression model revealed that the ResTCN achieves the smallest MAE, at 0.030, and the highest coefficient of determination, at 0.716, which are indicative of its proximity to the ground truth. These methodologies significantly enhance the quality of the training data. Subsequently, a Transformer–long short-term memory (T-LS) model was applied to identify and classify the lithology of unexplored wells. The input layer of the Transformer model follows an embedding-like principle for data preprocessing, while the encoding block encompasses multi-head attention, Add &amp; Norm, and feedforward components, integrating the multi-head attention mechanism. The output layer interfaces with the LSTM layer through dropout. A performance evaluation of the T-LS model against established rocky prediction techniques such as logistic regression, k-nearest neighbor, and random forest demonstrated its superior identification and classification capabilities. Specifically, the T-LS model achieved a precision of 0.88 and a recall of 0.89 across nine distinct lithology features. A Shapley analysis of the T-LS model underscored the utility of amalgamating multiple logging data sources for lithology classification predictions. This advancement partially addresses the challenges associated with imprecise predictions and limited generalization abilities inherent in traditional machine learning and deep learning models applied to lithology identification, and it also helps to optimize oil and gas exploration and development strategies and improve the efficiency of resource extraction.

Reservoir characteristics analysis and favorable area prediction of Zhuhai Formation, Wenchang A oilfield, South China Sea

Wenchang A oilfield is a recently discovered low-permeability oilfield in the western South China Sea. The exploration target is the Zhuhai Formation, which reservoir lithology changes significantly, and the distribution of favorable reservoirs is unclear. In this study, the reservoir characteristics of the Zhuhai Formation in the Wenchang A oilfield, South China Sea, were analyzed through core identification, thin section identification, physical property testing, pre-stack geostatistical inversion, and frequency-based AVO inversion. Pre-stack geostatistical inversion is based on geostatistics and combines the seismic inversion algorithm with the stochastic sequential simulation algorithm. While the frequency-varying AVO inversion method is an extension of the conventional AVO inversion method, with its core technology being spectrum decomposition. After frequency division, multiple data sets directly participate in the calculation, which enhances the stability and accuracy of inversion.We also predict the favorable reservoir areas of six small layers in the Zhuhai Formation. The results show that 1) the reservoir lithology of the Zhuhai Formation mainly consists of feldspar quartz sandstone and feldspar quartz sandstone, indicating high compositional maturity. The porosity of the reservoir ranges from 14.0% to 19.0%, with an average value of 16.3%. The permeability of the reservoir ranges from 3.1 mD to 126.1 mD, with an average value of 22.4 mD, indicating a medium porosity and low permeability reservoir. The supporting structure of a sandstone reservoir is particle-supported, with the main contact being the “point-line” contact between particles. The main types of pores in sandstone are primary intergranular pores and secondary intergranular dissolved pores. Some pores are feldspar-dissolved pores, and occasionally there are hetero-based micropore. 2) The diagenesis of the Zhuhai Formation reservoir mainly includes compaction, cementation, and dissolution. Cementation and dissolution have minimal impact on the physical properties of reservoirs. The porosity loss of the reservoir after compaction ranges between 16.0% and 27.9%, and the compaction rate ranges between 40.0% and 69.8%, indicating a medium compaction diagenetic facies. 3) The favorable areas of each layer of the Zhuhai Formation are mainly concentrated in the southwest of the study area. The upper layer of ZH1I, the upper layer of ZH1II and the lower layer of ZH1II exhibit the best physical properties, the thickest favorable sand body, the strongest oil and gas display, the widest range of favorable areas, and the greatest exploration potential. The favorable exploration potential of layer 1 in the lower part of ZH1I and layer 2 in the upper part of ZH1II is moderate. The second layer in the lower part of ZH1II has the poorest properties and lower exploration potential. The main factors affecting the favorable area include physical properties, oil and gas display, sand body thickness, etc.

Investigating the CO2 Geological Sequestration Potential of Extralow-Permeability Reservoirs: Insights from the Es1 Member of the Shahejie Formation in the Dawa Oilfield

Investigating the CO2 Geological Sequestration Potential of Extralow-Permeability Reservoirs: Insights from the Es1 Member of the Shahejie Formation in the Dawa Oilfield

A new rock physics model for fractured oil shale reservoirs in Mahu oil field

The exploration of unconventional reservoirs such as oil shale has become a focus of research in the oil/gas industry, but due to the diversity of lithology and structural complexity of shale reservoirs, the study of their rock physics laws is a challenge. By analyzing the physical, lithologic, and fluid characteristics of oil shale reservoirs in the Mahu area of Xinjiang, China, we adopted a variety of effective-medium theories to carry out rock physics modeling. We analyzed the differences in the calculation results of various theoretical models, and finally constructed a set of rock physics modeling processes suitable for oil shale reservoirs. The analysis shows that the calculation results of Voigt-Reuss-Hill (VRH) and Hashin-Shtrikman (HS) average for mixed mineral matrix are very similar, but there are certain differences between the upper and lower bounds calculated by them. The upper and lower bounds of HS average are closer than VRH average. We used the Kuster-Toksoz effective-medium, differential effective-medium (DEM), and self-consistent approximation models to construct the rock skeleton and analyzed the differences of the models. The results of the three models are almost identical, but their assumptions and limitations differ. We used DEM in the final shale model, considering the large porosity and the order of inclusion filling. According to the fracture distribution characteristics of oil shale reservoirs, we used an inclined fracture model to describe the fractures with different structural characteristics. Finally, the established shale rock physics model was used to calculate the actual logging data, and the results of elastic parameters are consistent with the measured data. Through the inversion of the fracture parameters, the inversion results are consistent with the measured results as a whole, indicating that the model has certain applicability to the simulation of oil shale reservoirs.

Classification and Prediction Method for Dolomite Reservoirs in the Permian Maokou Formation in the Sichuan Basin

The Lower Permian Maokou Formation in the Hechuan area is one of the key horizons for natural gas exploration in the Sichuan Basin, showing a low degree of exploration and great exploration potential. The Lower Permian gas reservoir is mainly controlled by the reservoir, therefore it is subject to the development of the Maokou Formation reservoir. However, due to the strong heterogeneity and significant variations in physical properties of the Maokou Formation dolomite reservoir in the Hechuan area, there are significant differences in seismic response characteristics of reservoirs with different porosities. Through comprehensive analysis of gas testing wells in the Maokou Formation, criteria for reservoir classification were established based on lithology, average porosity, plane porosity, and thickness of dolomite reservoirs. The effective reservoirs in the Maokou Formation were divided into two types: high-porosity reservoirs and medium-porosity reservoirs. On the basis of establishing a physical quantity model for the Lower Permian carbonate rock in the research area, the rock physical characteristics and seismic response characteristics of reservoirs with different pores are determined through model forward modeling, and appropriate methods are selected for prediction. By using the optimization processing method of gathers and wavelet decomposition to remove sidelobes, the issue of seismic abnormal response caused by lateral velocity changes in the underlying Maokou Formation has been solved. The relative strength relationship of seismic response amplitude can effectively characterize the true situation of reservoir development. Then, by using post-stack inversion and pre-stack simultaneous inversion methods, the high-porosity and medium-porosity dolomite reservoirs in the Maokou Formation are classified and predicted, thereby to effectively improve the prediction accuracy of reservoirs, ultimately providing a reliable basis for the exploration and development of the Maokou Formation in the Hechuan area in the later stage.

A composite water saturation model of continental mixed shale oil reservoirs based on complex lithology identification

The complex lithology, frequent interbedding and strong heterogeneity of the shale oil reservoir in Lucaogou Formation of the Middle Permian in Jimsar Sag lead to the complexity of litho‐electric parameters and the low accuracy of conventional reservoir saturation evaluation methods. Therefore, in this paper, automatic lithologic identification was firstly performed with the optimization inversion method of mineral components and K‐means clustering algorithm, and then, litho‐electric experiments were carried out to determine the litho‐electric parameters of different lithologies. Furthermore, the shale oil reservoir saturation calculation models of different lithologies were optimized and determined. Then, a composite water saturation model (Composite‐SW) based on complex lithology identification was constructed. Finally, the model was verified and analysed with the experimental results of water saturation. The coefficient of correlation between the calculation results of the Composite‐SW model and the core experiment results reached .88, indicating that the model was much better than conventional models. The model can be widely applied in logging evaluation of the continental mixed shale oil reservoir in the study area. The study provides a new idea for oil saturation evaluation of continental shale oil reservoirs and is significant for evaluating other types of shale oil reservoirs.