Diagenetic history and reservoir evolution of tight sandstones in the second member of the Upper Triassic Xujiahe Formation, western Sichuan Basin, China

Yang, Peng; Zhang, Likuan; Liu, Keyu; Cao, Binfeng; Gao, Jianlei; Qiu, Guiqiang

doi:10.1016/j.petrol.2021.108451

Cited by 21 publications

(18 citation statements)

References 28 publications

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“…Furthermore, there are thin source rocks, ordinary gas‐source conditions, and tight reservoirs in the Xu1 Member in Central Sichuan Basin. All these lead to the thin gas reservoirs, poor gas–water differentiation, and high water saturation of the gas reservoirs in the Xu2 Member (Sun et al, 2021; Yang et al, 2021; Gong et al, 2021). The different wells and different reservoir units in the vertical direction greatly differ in gas‐bearing property and single‐well output.…”

Section: Geological Settingmentioning

confidence: 99%

“…Gas fields such as Guang'an, Hechuan, and Tongnan have been discovered in the second, fourth, and sixth members of the Xujiahe Formation. This makes the Early‐Triassic Xujiahe Formation become one of the critical areas for natural gas reserves in the Sichuan Basin next to the Triassic Feixianguan and Permian Changxing formations (Yang et al, 2021; Zhao et al, 2010 & 2011). As of 2019, about 600 billion m 3 of proven natural gas reserves have been discovered in Central Sichuan Basin, 75% of which occur in the Xu2 Member (Zhao, Tang, Wang, & Chen, 2019).…”

Section: Introductionmentioning

confidence: 99%

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Pore structure and reservoir physical properties for effective development of tight sandstone gas: A case study from the Central Sichuan Basin, China

et al. 2022

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Several large gas fields have been found in the Central Sichuan Basin in recent years, with proven reserves of more than 100 × 10 9 m3. Among these, the tight sandstone gas in the Late Triassic Xujiahe Formation is a key target in the Sichuan Basin. The key to the discovery of high‐yield areas rich in tight sandstone gas is to determine the pore structures and the lower limits of effective physical properties of reservoirs that control natural gas accumulation. In this study, we investigate the Late Triassic second Member of the Xujiahe Formation (hereinafter referred to as the Xu2 Member) in the Central Sichuan Basin as an example. We analyse the distribution characteristics of pores and throats in the reservoirs and identify that the reservoir in the study area is mainly characterized by small pores and narrow throats. The physical properties of reservoirs in the area are mainly controlled by pore‐throat radius and the permeability–porosity ratio. Nuclear magnetic resonance analysis indicates that the irreducible water saturation is mostly more than 50% and is inversely correlated with the physical properties of reservoirs in the study area. Meanwhile, with an increase in the physical properties of reservoirs, the proportion of large pores increases and the irreducible water saturation rapidly decreases. According to the gas–water two‐phase seepage experiment, for reservoirs with good physical properties, the gas relative permeability significantly increases with an increase in gas saturation and the reservoir permeability has more significant effects. Where the reservoir permeability is less than 0.4 mD, the permeability of both gas phase and water phases is only less than 0.1, and the mobility of both gas and water is poor. Only when the reservoir permeability is more than 0.4 mD, can the gas relative permeability reach more than 0.3, and the water mobility is significantly inhibited. In this case, favourable results can be achieved from the natural gas development. As verified by the development of the tight sandstone reservoirs in the Xu2 Member in Central Sichuan Basin, the lower limits of physical properties allowing for effective natural gas development include the reservoir permeability of more than 0.1 mD (preferably more than 0.2 mD) and the reservoir porosity of greater than 6%–7%.

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Section: Geological Settingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Pore structure and reservoir physical properties for effective development of tight sandstone gas: A case study from the Central Sichuan Basin, China

et al. 2022

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“…The sedimentary facies are mainly about the braided river delta facies. The channels overlap with each other from different periods, forming a longitudinal superposition and plane contiguous sand bodies [31].…”

Section: Geologymentioning

confidence: 99%

“…The grain size varies from fine to medium, and the sorting property varies from poor to good. Moreover, the reservoir exhibits low porosity, low matrix permeability, and small pore-throat radii [31]. The microcracks maintain the reservoir connectivity and facilitate the gas production [33].…”

Section: Reservoir Characteristicsmentioning

confidence: 99%

Microcrack Porosity Estimation Based on Rock Physics Templates: A Case Study in Sichuan Basin, China

Ruan

Carcione

et al. 2021

Energies

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Low porosity-permeability structures and microcracks, where gas is produced, are the main characteristics of tight sandstone gas reservoirs in the Sichuan Basin, China. In this work, an analysis of amplitude variation with offset (AVO) is performed. Based on the experimental and log data, sensitivity analysis is performed to sort out the rock physics attributes sensitive to microcrack and total porosities. The Biot–Rayleigh poroelasticity theory describes the complexity of the rock and yields the seismic properties, such as Poisson’s ratio and P-wave impedance, which are used to build rock-physics templates calibrated with ultrasonic data at varying effective pressures. The templates are then applied to seismic data of the Xujiahe formation to estimate the total and microcrack porosities, indicating that the results are consistent with actual gas production reports.

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“…Permeability is an important parameter in tight sandstone reservoir evaluation and oil and gas field development and is the basis for establishing geological models, accurately estimating oil and gas reserves, and determining reasonable development plans [1][2][3][4]. However, because tight sandstone reservoirs have experienced complex diagenesis and exhibit strong heterogeneity, it is difficult to predict their permeability with high accuracy [5,6]. At present, the main methods for the high-precision prediction of permeability are (i) mathematical regression methods such as porosity and permeability regression based on petrophysical data or the response of logging curves [7][8][9][10]; (ii) theoretical modeling based on high-pressure mercury intrusion data, e.g., Win-land, Purcell, Swanson, and Katz-Thompson; [11][12][13] and (iii) prediction based on special logging series, e.g., nuclear magnetic resonance logging and dipole sonic logging [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Permeability Predictions for Tight Sandstone Reservoir Using Explainable Machine Learning and Particle Swarm Optimization

Liu

2022

Geofluids

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High-precision permeability prediction is of great significance to tight sandstone reservoirs. However, while considerable progress has recently been made in the machine learning based prediction of reservoir permeability, the generalization of this approach is limited by weak interpretability. Hence, an interpretable XGBoost model is proposed herein based on particle swarm optimization to predict the permeability of tight sandstone reservoirs with higher accuracy and robust interpretability. The porosity and permeability of 202 core plugs and 6 logging curves (namely, the gamma-ray (GR) curve, the acoustic curve (AC), the spontaneous potential (SP) curve, the caliper (CAL) curve, the deep lateral resistivity (RILD) curve, and eight lateral resistivity (RFOC) curve) are extracted along with three derived variables (i.e., the shale content, the AC slope, and the GR slope) as data sets. Based on the data preprocessing, global and local interpretations are performed according to the Shapley additive explanations (SHAP) analysis, and the redundant features in the data set are screened to identify the porosity, AC, CAL, and GR slope as the four most important features. The particle swarm optimization algorithm is then used to optimize the hyperparameters of the XGBoost model. The prediction results of the PSO-XGBoost model indicate a superior performance compared with that of the benchmark XGBoost model. In addition, the reliable application of the interpretable PSO-XGBoost model in the prediction of tight sandstone reservoir permeability is examined by comparing the results with those of two traditional mathematical regression models, five machine learning models, and three deep learning models. Thus, the interpretable PSO-XGBoost model is shown to have more advantages in permeability prediction along with the lowest root mean square error, thereby confirming the effectiveness and practicability of this method.

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Diagenetic history and reservoir evolution of tight sandstones in the second member of the Upper Triassic Xujiahe Formation, western Sichuan Basin, China

Cited by 21 publications

References 28 publications

Pore structure and reservoir physical properties for effective development of tight sandstone gas: A case study from the Central Sichuan Basin, China

Pore structure and reservoir physical properties for effective development of tight sandstone gas: A case study from the Central Sichuan Basin, China

Microcrack Porosity Estimation Based on Rock Physics Templates: A Case Study in Sichuan Basin, China

Permeability Predictions for Tight Sandstone Reservoir Using Explainable Machine Learning and Particle Swarm Optimization

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