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The Longmaxi Formation in the Sichuan Basin is a hotspot formation for shale gas exploration. In this paper, taking the Longmaxi Formation shale in Yuxi area as an example, a large number of TOC tests, X-diffraction whole-rock mineral fraction tests, electron microscope observations, physical properties, gas content, and CT scanning experiments were carried out, which in turn analyzed the characteristics of the shale gas reservoir and the factors influencing the gas content. The results of the study show that there are seven types of shale facies developed in Yuxi area, which are organic-rich siliceous shale facies (I), organic-rich carbonate-siliceous shale facies (II), organic-rich clayey shale facies (III), organic-poor clayey shale facies (IV), organic-poor carbonate-siliceous shale facies (V), organic-poor siliceous shale facies (VI), and organic-poor siliceous-carbonate shale facies (VII). The microstructural characteristics of the shale reflect that it was disturbed by multiple environmental factors at the time of deposition, and the sources of brittle minerals include both terrestrial clasts and biogenic siliciclastics. The bottom 30 m shale of Longmaxi Formation has good physical properties and gas content, which is a high-quality target section for shale gas exploration and development. TOC, Ro, permeability, quartz content and clay mineral content all have a significant effect on shale gas content and are important control factors for shale gas content. [ABSTRACT FROM AUTHOR]

With the gradual decline of natural gas production, reserve upgrading has become one of the important issues in natural gas exploration and development. However, the traditional reserve upgrade forecasting method is often based on experience and rules, which is subjective and unreliable. Therefore, a prediction method based on neural network is proposed in this paper to improve the accuracy and reliability of reserve upgrade prediction. In order to achieve this goal, by collecting the relevant data of natural gas exploration and development in Sichuan Basin, including geological parameters, production parameters and other indicators, and processing and analyzing the data, the relevant characteristics of reserves increase are extracted. Then, a neural network model based on multi-layer perceptron (MLP) is constructed and trained and optimized using backpropagation algorithm. The results show that the prediction accuracy of the constructed neural network model can reach more than 90% and can effectively predict the reserve upgrading. Experiments show that the model has high accuracy and reliability, and is significantly better than the traditional prediction methods. The method has good stability and reliability, and is suitable for a wider range of natural gas fields. [ABSTRACT FROM AUTHOR]

The exploration and development potential of shale gas reservoirs in the Sichuan Basin is enormous; however, it also faces difficulties such as complex structures, strong heterogeneity, and unclear geophysical response characteristics. Fine prediction of geostress is an important part of shale gas exploration and development, which directly affects the implementation effect of reservoir evaluation, well trajectory design, and fracture reconstruction. The existing geostress prediction techniques lack high-precision seismic data constraints, making it difficult to accurately reflect the planar distribution characteristics of geostress in the block with rapid changes in complex tectonic zones. At the same time, the geophysical response characteristics of geostress in the Sichuan Basin are unknown, and the geostress seismic prediction technology lacks theoretical basis. This paper combines numerical simulation and physical experiments and defines the characteristics of the geophysical response of shale gas reservoirs in the Sichuan Basin changing with the stress field, and technical countermeasures for geostress seismic prediction have been established to provide technical means for accurate prediction of the geostress field in the shale gas block. Based on the geostress sensitive parameters obtained from prestack seismic inversion, the geostress field prediction of a shale gas work area in the Sichuan Basin is realized. [ABSTRACT FROM AUTHOR]

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The shale of the Wufeng–Longmaxi formation in the Sichuan Basin is the preferred layer for shale gas exploration in China, and its petrophysical characteristics are the key to geological and engineering sweet spot prediction. However, the characteristics and impact mechanisms of its acoustic wave velocity and elastic anisotropy are currently unclear. In this paper, the Wufeng–Longmaxi shale is taken as the research object, and the P-wave and S-wave velocities of the samples are tested under the loading and unloading processes of confining pressure. The stress sensitivity variations in parameters such as wave velocity, wave velocity ratio, and anisotropy are discussed. P-wave and S-wave anisotropy parameters are correlated under different pressure conditions. X-ray diffraction, casting thin sections, scanning electron microscopy, micron CT scanning, and other analytical techniques are used to explore the mechanisms of stress sensitivity of elastic parameters. The research results indicate that: (1) the acoustic velocities of samples from different angles are V90° > V45° > V0°, and there is a positive correlation between the wave velocity and the confining pressure. After unloading the confining pressure, irreversible plastic deformation occurs due to the closure of some microfractures in the rock core, causing the wave velocity to be higher than the initial value. (2) The stress sensitivity coefficient of the P-wave (The mean is 3.00 m·s−1·MPa−1) is higher than that of the S-wave (the mean is 1.23 m·s−1·MPa−1), and the stress sensitivity coefficient of the compacted stage (the mean is 3.02 m·s−1·MPa−1) is higher than that of the elastic stage (the mean is 1.21 m·s−1·MPa−1). (3) The anisotropy of the P-wave and S-wave is negatively correlated with the confining pressure. When the confining pressure is loaded to 65 MPa, the change rate of the P-wave anisotropy coefficient is 23%, and its stress sensitivity is higher than that of S-wave anisotropy coefficient (the change rate is 13.7%). After unloading the confining pressure, the degree of anisotropy is reduced due to the closure of some microfractures. The empirical formula of P-wave and S-wave anisotropy parameters under different pressures is established through linear regression, which can provide a reference for mutual predictions. (4) The variation in wave velocity anisotropy with stress can be divided into stress and material anisotropy, which are related to the directional arrangement of microfractures and clay minerals, respectively. The quantitative characterization of shale anisotropy can be realized by evaluating the development degree of reservoir fractures and mineral components, providing a reference for logging interpretations, sweet spot prediction, and fracturing construction of shale gas reservoirs. [ABSTRACT FROM AUTHOR]

The sedimentary environment and organic matter enrichment relationship of the Upper Ordovician Wufeng Formation black shale, Guanyinqiao mudstone, and Lower Silurian Longmaxi Formation black shale in the Sichuan Basin of Weiyuan are analyzed using geochemical methods such as organic carbon, sulfur, major elements, and trace elements. The experimental results illustrate that the upper section of the Wufeng Formation and the lower section of the Longmaxi Formation are organic matter enrichment layers. The presence of P indicates a high productivity level in the Sichuan Basin from the Late Ordovician to the Early Silurian. In addition, indicators such as V/Cr, Ni/Co, and S/C suggest that the Wufeng Formation was deposited under anoxic reductive conditions, that the ice age Guanyinqiao segment was in an oxygen-rich to oxygen-poor environment, and that the Longmaxi Formation was in a sulfidic environment. Mo/TOC indicates that the Wufeng Formation shale was controlled by a restricted basin and that the Guanyinqiao segment and the Longmaxi Formation were in a medium-to-weak retention environment. The weak correlation of TOC with P/Al and Al indicates that the level of primary productivity and terrigenous detritus had a minor effect on the organic matter enrichment of the Wufeng and Longmaxi Formation black shale. Conversely, the positive correlation of TOC with V/Cr and Ni/Co illustrates that the anoxic reductive sedimentary environment is the main factor affecting the organic matter enrichment of the Wufeng and Longmaxi Formation black shale. Based on these studies, the development model of organic-rich shales of the Ordovician–Silurian in Weiyuan, Sichuan Basin is proposed. This paper may provide a reference for shale gas exploration in the Wufeng–Longmaxi Formation and a sedimentary response to the major geological events of Ordovician–Silurian. [ABSTRACT FROM AUTHOR]

Microfractures can connect isolated pores within shale, significantly increasing the shale's storage capacity and permeability, and benefiting shale gas exploitation. Therefore, the quantitative characteristics of microfractures are important parameters for shale reservoir evaluation. In this paper, taking the Jurassic Da'anzhai Member (J1z4) lacustrine shale in the Yuanba area of the northern Sichuan Basin as an example, we propose a method for comprehensive and quantitative characterization of shale microfractures that combines rock thin section (RTS) and scanning electron microscopy (SEM) observations. The different magnifications of RTSs and SEM images lead to the identification and characterization of microfractures of different scales using these two methods. RTSs are mainly used to characterize microfractures with widths larger than 10 μm, while SEM is mainly used to characterize microfractures with widths smaller than 10 μm. These techniques can be combined to comprehensively and quantitatively characterize microfractures of different scales in shale. The microfracture characterization results show that the average total porosity of the J1z4 shale is 4.46%, and the average microfracture surface porosity is 1.20% in the Yuanba area. The calculated average percentage of microfracture porosity to total porosity is 21.09%, indicating that the J1z4 shale reservoir space is dominated by pores and has the conditions for stable shale gas production and potential for shale gas exploration. However, the percentage of microfracture porosity to total porosity of shale near faults and fold zones approaches or exceeds 50%, which may lead to the loss of shale gas. The new method proposed in this study is also useful for quantitative characterization of shale microfractures in the Sichuan Basin and other basins. [ABSTRACT FROM AUTHOR]

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Copyright of Natural Gas Industry is the property of Natural Gas Industry Journal Agency and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Natural Gas Industry is the property of Natural Gas Industry Journal Agency and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Natural Gas Industry is the property of Natural Gas Industry Journal Agency and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The total organic carbon content (TOC) is a core indicator for shale gas reservoir evaluations. Machine learning-based models can quickly and accurately predict TOC, which is of great significance for the production of shale gas. Based on conventional logs, the measured TOC values, and other data of 9 typical wells in the Jiaoshiba area of the Sichuan Basin, this paper performed a Bayesian linear regression and applied a random forest machine learning model to predict TOC values of the shale from the Wufeng Formation and the lower part of the Longmaxi Formation. The results showed that the TOC value prediction accuracy was improved by more than 50% by using the well-trained machine learning models compared with the traditional Δ Log R method in an overmature and tight shale. Using the halving random search cross-validation method to optimize hyperparameters can greatly improve the speed of building the model. Furthermore, excluding the factors that affect the log value other than the TOC and taking the corrected data as input data for training could improve the prediction accuracy of the random forest model by approximately 5%. Data can be easily updated with machine learning models, which is of primary importance for improving the efficiency of shale gas exploration and development. [ABSTRACT FROM AUTHOR]

Brittleness is important in the evaluation of the fracturing ability of shale reservoir and has a significant impact on shale gas exploration and development. This paper discusses the characteristics and controlling factors of brittleness of continental shale in the Da'anzhai Member of the Ziliujing Formation of Lower Jurassic age in the northeast Sichuan Basin. Continental shale lithofacies and their associations were grouped into four main rock types: clayey shale, silty shale, shell calcareous clayey shale, and silty clayey shale, characterized by the high clay content and local enrichment of carbonate minerals as a whole. Compared with the marine shale, the continental shale contained a low content of siliceous minerals, a high content of carbonate minerals, and a large number of shell limestone interlayers. Carbonate minerals play an important role in controlling the brittleness of continental shale. The shale interlayers were mainly shell limestone interlayers with a thickness of several centimeters and a large number of shell laminates with thicknesses of several millimeters were also observed. The shell laminates were mainly filled with calcite. Due to the dissolution process, a large number of bedding joints and corrosion joints were formed in the calcite shell layers. In the interlayers with a high shell content, a large number of microfractures developed. The energy consumption required for maintaining fracture expansion was lower after fracturing; the fractures greatly improved the reservoir's brittleness. [ABSTRACT FROM AUTHOR]

The shale gas exploration in Changning, Weiyuan, Fuling Jiaoshiba, and other areas has achieved great success in the Sichuan Basin, and the basin margin of the Sichuan Basin will be an important target in the future. In view of the lack of understanding of shale gas reservoir characteristics of the basin margin area of the Sichuan Basin, this paper conducts a study on reservoir characteristics and gas-bearing properties of this area using qualitative and quantitative techniques. The results show that the thickness of the black shale reservoir in the Qianjiang area is up to 33 m, the kerogen sapropelic type (I) is dominant, and Ro ranges from 2.2 wt% to 3.39 wt%. The mineral composition is relatively complex, mainly composed of quartz and clay minerals. Quartz content is in the range between 9.4 wt% and 79 wt% (average = 47.48 wt %), while clay content is in the range between 10.02 wt% and 43.6 wt% (average = 29.41 wt %). There are various types of reservoir spaces, mainly including organic pores dominated by matrix pores, intergranular and intragranular pores, intergranular and intragranular dissolution pores, secondary dissolution pores, and microfractures, among which organic pores are the main pore types. The pore volume of nanopores is 0.009897-0.017177 cm3/g (average = 0.01304 c m 3 / g), and the specific surface area ranges from 12.1 m2/g to 35.1 m2/g (average = 20.47 m 2 / g). Micropores and mesopores are the main pore sizes for the development of organic pores. Quartz particles promote the development of reservoir space, while clay minerals are not conducive to the development of pores. The total gas content ranges from 0.5 m3/t to 4.43 m3/t (average = 1.69 m 3 / t). Its enrichment is controlled by many factors. The increase of organic matter abundance is the material basis for shale gas enrichment. The development of organic pores provides high-quality storage space for shale gas preservation. The development of different mineral components restricts the enrichment of shale gas. [ABSTRACT FROM AUTHOR]

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Neoproterozoic strata are widely developed in the Upper Yangtze region of South China, among which the Sinian Dengying Formation has been discovered with numerous large gas fields in the Sichuan Basin, and hence they have become an essential domain for natural gas exploration and development. The early‐to‐middle Neoproterozoic Nanhua System and Doushantuo Formation are characterized by rifting deposits in the northeastern margin of Sichuan Basin, but their depositional succession and sequence architectures are still unclear, which has largely restricted our understanding of the resource potential of these strata. In this paper, based on the outcrops of the Neoproterozoic Nanhua System and Doushantuo Formation in the northeastern margin of Sichuan Basin, we describe the stratigraphic distribution and sedimentary evolutionary characteristics, and establish the regional stratigraphic framework, sedimentary cycle and evolutionary sequence of the Nanhua System and Doushantuo Formation. The results show that the lithology of the Nanhua System and Doushantuo Formation is primarily composed of conglomerate, conglomeratic sandstone, medium‐fine sandstone, siltstone, mudstone, shale and tillite; a variety of sedimentary facies including alluvial fan, river, delta, coastal shore, shelf, basin and tillite were developed; five third‐order sequences could be identified, representing multiple cycles of marine transgression and regression. The climate during the deposition of the Nanhua System and Doushantuo Formation underwent from a warm and humid pre‐glacial period through cold and arid multiple glacial periods to a warm and humid interglacial period, which consequently formed a sedimentary sequence of alluvial fan and fluvial delta to tillite during the early period, shelf basin to tillite during the middle period and slope basin to delta facies during the late period. In terms of a plan view, the depositional pattern is composed of alluvial fans and deltas at the proximal part of the rift, deep‐water shelf to slope at the middle part of the rift and slope to basin facies at the central part of the rift. Finally, we propose that large‐scale distribution of high‐quality source rocks developed in the interglacial Datangpo and Doushantuo formations, while fluvial‐deltaic reservoirs occurred in the Gucheng, Nantuo and Doushantuo formations, thus showing favourable resource potential and exploration prospects within the Sichuan Basin. [ABSTRACT FROM AUTHOR]

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Parameters such as gas content (GAS), porosity (PHI) and total organic carbon (TOC) are key parameters that reveal the shale gas geological "sweet spot" of reservoirs. However, the lack of a three-dimensional high-precision prediction method is not conducive to large-scale exploration of shale gas. Although the parameter prediction accuracy based on well logging data is relatively high, it is only a single point longitudinal feature. On the basis of prestack inversion of reservoir information such as P-wave velocity and density, high-precision and large-scale "sweet spot" spatial distribution predictions can be realized. Based on the fast growing and widely used deep learning methods, a one-dimensional convolutional neural network (1D-CNN) "sweet spot" parameter prediction method is proposed in this paper. First, intersection analysis is carried out for various well logging information to determine the sensitive parameters of geological "sweet spot". We propose a new standardized preprocessing method based on the characteristics of the well logging data. Then, a 1D-CNN framework is designed, which can meet the parameter prediction of both depth-domain well logging data and time-domain seismic data. Third, well logging data is used to train a high-precision and robust geological "sweet spot" prediction model. Finally, this method was applied to the WeiRong shale gas field in Sichuan Basin to achieve a high-precision prediction of geological "sweet spots" in the Wufeng–Longmaxi shale reservoir. [ABSTRACT FROM AUTHOR]

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Copyright of Natural Gas Industry is the property of Natural Gas Industry Journal Agency and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)