Marine-continental transitional (MCT) shale gas is an important successor of unconventional natural gas resource in China. Based on integrated analyses of published data including outcrop investigation, exploration practice, drilling cores, and experimental testing, the recent progresses of both global and domestic shale gas development were systematically reviewed and compared, and we further examined the exploration progress and challenges of MCT shale gas in the Ordos Basin, Sichuan Basin, and their adjacent areas, and conducted a comprehensive discussion of the key geological conditions for the formation of shale gas and its resource potential, challenges, and counter measures. The results show that MCT shale in China is mainly developed within the Carboniferous–Permian strata (Benxi, Shanxi, and Longtan formations), dominated by lagoon, swamp, and tidal flat facies, and possesses favorable conditions for shale gas formation and development potential. They mainly include the generation and storage of shale gas as follows: (1) The organic-rich intervals are thick and widespread, dominated by Type III organic matter with high total organic carbon (TOC) content (⩾3.0

Unconventional natural gas has become an important contributor to the growth of global natural gas reserves and production expansion. However, pronounced heterogeneity among different reservoir types results in substantial variations in enrichment mechanisms, sweet-spot formation, and development responses, thereby hindering the establishment of a unified framework for unconventional gas development geology. This study systematically compares shale gas, tight gas, shallow coalbed methane (CBM), and deep CBM within an integrated framework that links enrichment mechanisms, sweet-spot evaluation, and development response. The results reveal distinct geological controls among these unconventional systems. The shale gas is characterized by source–reservoir integration, nanoscale storage, and favorable preservation conditions; the tight gas is controlled by near-source charging and permeability-limited flow; the shallow CBM is dominated by adsorption-controlled enrichment; and the deep CBM exhibits a coupled accumulation mode involving both free and adsorbed gas. Sweet spots are defined as reservoir intervals where favorable geological enrichment conditions can be effectively converted into engineering recoverability. Their identification requires the integrated evaluation of geological conditions, engineering parameters, and production performance rather than relying on individual indicator anomalies. Development responses reflect the evolution of enrichment characteristics during stimulation and production. Shale gas productivity is primarily governed by fracture-network conductivity and matrix gas supply, whereas tight gas development depends on reservoir connectivity and geological heterogeneity. Shallow CBM production is controlled by drainage-induced desorption and subsequent gas transport processes, whereas deep CBM production is characterized by an initial stage dominated by free-gas recovery followed by increasing adsorbed gas contributions. This study demonstrates that effective unconventional gas development requires an integrated understanding of enrichment mechanisms, sweet-spot evaluation, and production responses. Future research should focus on the quantitative characterization of enrichment processes, dynamic prediction of sweet-spot distribution, and cross-scale production modeling to improve the efficiency of unconventional gas development under complex geological conditions.

Based on China’s latest exploration and development achievements, production performance data of over 7 000 horizontal wells, and the Unconventional Oil & Gas Digital-Intelligent Platform (UOG), and by integrating statistical analysis and machine learning prediction techniques, this study systematically compares four types of unconventional natural gas (tight gas, shale gas, shallow coalbed methane and medium–deep coal-rock gas) in the country, from the aspects of resource characteristics, key technologies, development indicators and prospects. China holds a substantial quantity of unconventional natural gas, especially shale gas and medium–deep coal-rock gas which boast prominent resource advantages and present a large-scale “continuous” spatial distribution. More than 75% of high-quality resources are concentrated in the Ordos and Sichuan basins. A type-adaptive key technical system has been established, incorporating extensive recovery of tight gas by virtue of “well pattern optimization + low-cost fracturing”, commercial development of shale gas relying on “geological-engineering dual sweet spot evaluation + super fracture network fracturing”, stable production of shallow–medium coalbed methane through “precision drainage and depressurization”, and breakthroughs in pilot technologies such as pressure-controlled development and energy-gathered fracturing for horizontal wells of medium–deep coal-rock gas. The four types of unconventional natural gas vary significantly in development indicators. Tight gas, shale gas and medium–deep coal-rock gas reach peak production 10–30 days after gas breakthrough, showing the characteristics of high initial production followed by rapid decline (with a first-year decline rate of 30%–51%). Specifically, shale gas horizontal wells have the highest average daily production in the first year (7.28×104 m3/d on average) and single-well estimated ultimate recovery (EUR) (8 255×104 m3 on average). Shallow coalbed methane reaches peak production about 240 days after gas breakthrough, presenting a trend of slow rise–gentle decline, with the lowest single-well indicators. At present, the development of unconventional natural gas is faced with four major constraints including complex geology, technical bottlenecks, environmental restrictions and imperfect policies. It is necessary to address the predicament through multi-dimensional coordination in terms of resources, technology, environmental protection and policies.

Fluid injection and nano-scanning CT (nano-CT) are pivotal for quantitative shale pore characterization, yet the reliability of nano-CT results is challenged by human-induced biases in data processing, restricting practical implementation. This study addresses this by presenting a computational framework for synchrotron radiation nano-CT data analysis focused on transitional shale, emphasizing software-driven methodologies to enhance objectivity. Detailed workflows for automated image segmentation and feature extraction are described, with preprocessing stages utilizing specialized image processing software to correct beam-hardening artifacts and eliminate ring noise. Gaussian filtering algorithms are applied to denoise, smooth textures, and optimize contrast, minimizing manual intervention in image enhancement. Complementary scanning electron microscopy and fluid injection experiments enable multi-scale validation, facilitating comprehensive pore characterization across dimensions. Key findings include: (1) Mesopores constitute 74% of transitional shale pore volume, with clay-associated inorganic pores dominant and organic pores scarce; (2) Pore morphology is primarily elliptical, evolving toward flattened, elongated geometries with increasing size, a trend systematically captured through algorithmic shape analysis; (3) Cross-validation with high-pressure mercury injection (HPMI) demonstrates that nano-CT excels in resolving pore geometry and seepage pathways, while HPMI is optimal for quantifying pore volume and size distribution. By integrating computational image processing techniques, this framework establishes a reproducible, software-centric methodology to mitigate human bias in nano-CT data analysis. The approach underscores the critical role of algorithmic preprocessing and automated feature extraction in enhancing the reliability of shale pore characterization, providing a robust foundation for improving reservoir evaluation through multi-modal experimental integration.

The objective of this study is to analyze dominant controlling factors of the EUR of shale gas wells and then to forecast the EUR precisely by employing knowledge graph and automated machine learning techniques. First, an ontology knowledge representation model and a set of classification system for shale gas production are constructed, which include 13 shale gas objects such as basin, shale gas play, shale gas field, shale gas reservoir, and shale gas well, and their 112 geological, engineering and production parameters, such as mineral brittleness, fracturing section length, sanding intensity, and first-year production, and so on. Subsequently, structured data from existing databases are transformed, and loaded into the knowledge base. Large amount of unstructured data from papers, presentations, professional books are extracted and loaded by using various natural language processing (NLP) tools. The final shale gas knowledge base contains 56 shale gas plays and more than 1,000 shale gas wells worldwide. Based on the shale gas knowledge base, the graph embedding algorithm is used to convert the graph into a vector in order to train the machine learning models. Various automated machine learning frameworks such as TPOT, H2O, Auto-Sklearn, and AutoGluon are implemented and the performances are compared. According to the model with best performance, the main controlling factors of the EUR of shale gas wells are high-quality bed thickness, fracturing section length, and fracturing fluid volume, etc., which are consistent with shale gas production practices. The MSE and MAE of the best model on the testing dataset are 0.06 and 0.19, respectively. The approach of knowledge base construction and application developed in this paper can be extended to the entire life cycle of E&P process, which can make full use of various documents, data and knowledge accumulated in the oil and gas industry to conduct decision support.

A major challenge in transient pressure analysis for shale gas wells is their complex transient flow behavior and fracturing parameters. While numerical simulations offer high accuracy, analytical models are attractive for transient pressure analysis due to their high computational efficiency and broad applicability. However, traditional analytical models are often oversimplified, making it difficult to capture the complex seepage system, and three-dimensional fracture characteristics are seldom considered. To address these limitations, this study presents a comprehensive hybrid model that characterizes the transient flow behavior and analyzes the pressure response of a fractured shale gas well with a three-dimensional discrete fracture. To achieve this, the hydraulic fracture is discretized into several panels, and the transient flow equation is numerically solved using the finite difference method. Based on the Langmuir adsorption isotherm and the pseudo-steady diffusion in matrix and Darcy flow in the network of micro-fractures, a reservoir model is established, and the Laplace transformation is adopted to solve the model analytically. The transient responses are obtained by dynamically coupling the flow in the reservoir and the discrete fracture. The precision of the proposed model is validated using the commercial numerical simulator, Eclipse. A series of transient pressure dynamic curves are drawn to make a precise observation of different flow regimes, and the effects of several parameters on transient pressure response are also examined. The results show that the shale gas well testing interpretation curves comprise nine flow stages. The pressure drop of shale gas reservoirs is lower than that of conventional gas reservoirs due to the replenishment of desorbed gas. The artificial fracture flow capacity, fracture length, and height are the main engineering factors affecting the pressure responses of shale gas wells. Maximizing the degree and scope of reconstruction can enhance the gas well production capacity during fracturing construction. The research results also indicate that our model is a reliable semi-analytical model for well test interpretations in real case studies.

The rapid expansion of shale gas extraction worldwide has raised significant concerns about its impact on water resources. China is expected to undergo a shale revolution following the U.S. Most of the information on water footprint of shale gas exploration and hydraulic fracturing has been focused on the U.S. Here, we addressed this knowledge gap by establishing a comprehensive database of shale gas extraction in China, utilizing operational data from over 90 % of shale gas wells across the country. We present systematic analysis of water usage and flowback and produced water (FP water) production from all the major shale gas fields in China. Between 2012 and 2022, a total of 2740 shale gas wells were hydraulically fractured in China, primarily located in Sichuan and Chongqing Province. About 113 million m(3) water was used for hydraulic fracturing, resulting in a cumulative shale gas production of 116 billion m(3). As of 2022, the annual water use for hydraulic fracturing exceeded 20 million m(3), and the annual FP water production reached 8.56 million m(3). Notably, 80 % similar to 90 % of the FP water has been reused for hydraulic fracturing since 2020, accounting for 29 % to 35 % of the annual water usage for hydraulic fracturing. Water use per well in China varies primarily between 21,730 m(3) to 61,070 m(3) per well, and water use per horizontal length ranges primarily between 20 m(3)/m and 35 m(3)/m. The average ultimate FP water production per well in China was estimated to be 22,460 m(3). The water use intensity (WUI) for shale gas extraction in China mainly ranges from 7 to 25.4 L/GJ, which is significantly higher than that of the U.S. This disparity is largely due to the lower Estimated Ultimate Recovery (EUR) of shale gas wells in China. Despite the considerable water consumption during the hydraulic fracturing process, shale gas has a relatively low water footprint compared to other conventional energy resources in China. The Produced water intensity (PWI) for shale gas extraction in China ranges from 3.9 to 7.3 L/GJ, which is consistent with the previously reported PWI values for shale gas extraction in the U.S. This study predicts water usage and FP production spanning the period 2023 to 2050 under two scenarios to assess the potential impact of shale gas extraction on water resources in the Longmaxi shale region in Sichuan Basin. The first scenario assumed a constant drilling rate, while the second assumed a yearly 10 % increase in drilling rate. With an assumed FP water reuse rate of 85 % for hydraulic fracturing, the estimated annual freshwater consumption for the two scenarios is 10.4 million m(3) and 163 million m(3), respectively. This accounts for only 0.28 parts per thousand and 4.4 parts per thousand of the total annual surface water resources in Sichuan and Chongqing Province. Our findings suggest that freshwater usage for hydraulic fracturing in humid Southern China is small relative to available surface water resources. However, prospective large-scale shale gas extraction in other arid and semi-arid regions may enhance the regional water scarcity. It is necessary to develop new hydraulic fracturing technologies that can use saline groundwater or other types of marginal water, and explore alternative management and treatment strategies for FP water.

To evaluate the production performance of shale gas wells accurately, a quadruple-porosity medium model based on kerogen, the inorganic matrix, and natural-fracture-hydraulic-fracture network is proposed to simulate shale gas production. This model includes an apparent-kerogen permeability model to characterize the combined effects of gas slippage, Knudsen diffusion, surface diffusion, stress sensitivity, and matrix shrinkage. The fractal theory is used to characterize the non-equidistant distribution of secondary fractures in the hydraulic fracture network quantitatively. In addition, the effect of the stress sensitivity of the hydraulic fracture network on the production is considered, and a numerical method for the quadruple-porosity model is presented. After the validation of the model with field production data, the sensitivity parameters were analyzed to study the effects of the apparent kerogen permeability, Langmuir volume, fractal dimension, primary and secondary fracture conductivities, geomechanical effect, stimulated reservoir volume (SRV), and inorganic-matrix permeability on the gas production. This study provides a theoretical model for a more accurate evaluation of the shale gas production and improves the shale gas seepage theory.

The boost of shale gas production in the last decade has reformed worldwide energy structure. The macroscale modeling of shale gas production becomes particularly important as the economic development of such resources relies on the deployment of expensive hydraulic fracturing and the reasonable planning of well schedules. A flood of literature was therefore published focused on accurately and efficiently simulating the production performance of shale gas and better accounting for the various geological features or flow mechanisms that control shale gas transport. In this regard, this paper presents a holistic review of the macroscopic modeling of gas transport in shale. The review is carried out from three important points of view, which are the modeling of the gas flow mechanisms, the representation of multiscale transport, and solution techniques for the mathematical models. Firstly, the importance of gas storage and flow mechanisms in shale is discussed, and the various theoretical models used to characterize these effects in the continuum scale are introduced. Then, based on the intricate pore structure and various pore types of shale gas reservoirs, this review summarizes the multiple-porosity models in the literature to represent multiscale gas transport, and discusses the applicability of each model. Finally, the numerical and analytical/semi-analytical approaches used to solve the macroscopic mathematical model governing shale gas production are reviewed, with a focus on the treatment of the complex fracture network formed after multistage hydraulic fracturing.

Accurate prediction of shale gas well production and estimated ultimate recovery (EUR) is always a difficult and hot spot in shale gas development. In particular, the production and EUR prediction of shale gas wells in new production blocks are faced with the lack of field gas well data and the difficulty of model development. In view of the above problems, this study proposes a new deep transfer learning strategy, which uses transfer component analysis (TCA) and deep neural network (DNN) to achieve shale gas well production and EUR prediction across formations/blocks. The feature extractor based on TCA can narrow the input feature distribution of the source and the target domains. The neural network model can be used to establish a domain -adaptive transfer learning model without the prediction performance degradation caused by distribution offset. Validity and accuracy of the model were analyzed using gas well data from Weiyuan and Luzhou blocks in Sichuan Basin, China. The results appear that the reasonable application of TCA can greatly improve the prediction performance of shale gas well transfer learning model. For data sets of the same size, compared with the transfer learning model developed by classical machine learning algorithms, the proposed neural network-based transfer learning model can significantly improve the accuracy of production prediction across formations/ blocks. In addition, the proposed model can also be extended to other types of oil and gas production prediction tasks cross formations/blocks.

In 2012, China's first national shale gas demonstrations areas were set up in the Sichuan Basin. After 10 years' construction and practice, the giant marine shale gas area of 10 × 1012 m3 level is built up in the Sichuan Basin, and shale gas steps into a new stage of large-scale benefit exploration and development. In order to systematically summarize the achievements in shale gas exploration and development and provide guidance and reference for the exploration and development of deep shale gas and shale oil & gas in other areas, this paper systematically summarizes the main characteristics, development models and key identification and evaluation technologies for quality reservoirs of Wufeng Formation–Longmaxi Formation shale gas by analyzing the electric property, lithofacies, reservoir parameters and microscopic porosity of key wells in the basin. And the following research results are obtained. First, biogenetic siliceous shale, calcareous shale and mixed shale are main lithofacies types in quality shale gas reservoirs, and they are formed in the environment of semi-deep and deep water continental shelf. Their lateral distribution is controlled by the paleogeomorphology and their vertical development is influenced by provenance, redox condition and paleo productivity. They are 25–90 m thick. Second, in the quality shale gas reservoirs develop organic pores, inorganic pore and microfractures (including lamina/bedding fractures), among which, organic pore is one of the main reservoir spaces and microfracture is not only indispensable reservoir space, but also production pathway. The reservoir space of shale gas is overall micro-nano pore, and macropores play an important role in shale gas enrichment. Third, three development models of quality reservoir are established, including sedimentary type, diagenesis type and reworking type. The sedimentary type is the foundation. Multiple quality reservoirs are developed in the high U/Th interval of graptolite belt at the bottom of Longmaxi Formation, and their thickness is mainly controlled by paleogeomorphology and especially greater in the depression area. The diagenesis type is divided into three forms, i.e., syngenetic-early diagenetic rigid support, middle-late diagenetic mineral-organic matter transformation, and overpressure relief compaction. The reworking type is dominated by quality reservoirs with microfractures. Fourth, the core technologies for identifying and evaluating quality shale gas reservoir include large-size core and rock slice observation, high-accuracy rock mineral identification, experimental gas content test and simulation, SEM microscopic characterization, 3D microscopic pore reconstruction, comprehensive geophysical interpretation and prediction and big data analysis. In conclusion, nearly 10 years' research and practice achievements in demonstration area construction can deepen the understanding on domestic quality shale gas reservoirs, promote the effective development of the theories and technologies related to shale gas reservoirs, improve the prediction accuracy of shale gas sweet spot zones/intervals, and expand the shale gas exploration and development achievements of demonstration areas.

The exploration and development theory and technology of marine shale gas with shallow burial depth of 3500 m in South China has become mature after more than 10 years of practice. In order to continuously promote the exploration and development of shale gas in China, based on the research results of many scholars, combined with production practice, the main characteristics of marine shale gas in China and the main theory and technology of exploration and development are further summarized. The results show that: (1) The basic characteristics of marine shale gas in southern China are marine deep-water shelf deposition, and organic-rich shale is distributed continuously in a large area; The organic matter is stored in situ after high thermal maturity gas generation, and the gas content of shale is affected by late tectonic activity; The reservoir space is dominated by nano-scale pores, and the reservoir is super-tight and ultra-low permeability, which has no natural productivity without well stimulation; Bedding and natural fracture affect the productivity of shale gas wells; The mechanical properties of shale rock determine the effect of horizontal wells. In the early stage of well production, the production rate is high and the decline is fast, while in the middle and late stage, the rate is low but the production cycle is long. (2) Based on the above characteristics, the theory of "sweet spot" and "sweet interval" of shale gas and the theory of effective development of artificial gas reservoir are established, that is, the sedimentary and tectonic interaction forms the "sweet area" or "sweet spot" of marine shale gas. Gas productivity is determined by geological and engineering factors, so the "sweet spot" must be found to develop. By constructing artificial gas reservoir through "making an artificial high permeability area, and reconstructing a seepage field", the effective development of ultra-tight and low permeability shale gas reservoirs can be realized. It is the most effective technique for shale gas development to accurately create "transparent geological body" of reservoirs based on geology-engineering integrated evaluation technology and establish artificial gas reservoir through multi-stage fracturing technology. (3) The overpressure area in the southern Sichuan Basin is the "sweet area" for shale gas development. The "sweet interval" of the lower Silurian Longmaxi Formation with a thickness of 3–5 m and high brittle-rich organic matter is the optimal "golden target" for horizontal wells. (4) The development leapfrog from the first generation to the second generation has been realized by the key technology of multi-stage fracturing of horizontal wells. The EUR per well of Upper Ordovician Wufeng Formation and Longmaxi Formation in southern Sichuan has increased from 0.5 × 108 m3 in the initial stage to 1.0 × 108–1.2 × 108 m3 at present with a buried depth of shallower than 3500 m. (5) The effective area favorable for shale gas development in southern Sichuan is 2.0 × 104 km2, and it is estimated that the proven geological reserves of shale gas can reach 10 × 1012 m3. It is preliminarily predicted that the shale gas production in China will exceed 300 × 108 m3 in 2025 and reach 400 × 108 m3 in 2035.

"甜点"是页岩气储层中相对高产的层位和区域,地质甜点、工程甜点和综合甜点的合理预测及评价,是页岩气规模效益开发的基础之一.针对页岩气储层甜点多参数综合定量评价,引入层次分析法,综合地质与工程要素,基于高分辨率三维地质模型,形成了地质工程一体化页岩气甜点评价的新方法,进行页岩气储层甜点区域的预测.首先,综合前人研究成果,建立了一种页岩气地质甜点、工程甜点和综合甜点评价的指标体系;随后,设计了基于层次分析法的页岩气储层地质工程一体化甜点评价方法的技术路线;最后,采用昭通页岩气田海坝区块X井区实例数据,基于该区域高分辨率三维地质模型,根据形成的甜点评价方法,进行了研究区地质甜点、工程甜点和综合甜点的预测和评价.结果表明,该方法可以综合地质和工程的多种评价指标,实现了昭通页岩气田海坝区块X井区地质甜点、工程甜点和综合甜点的评价,评价的甜点区域主要分布在奥陶系五峰组,志留系龙马溪组一段1亚段1小层、2小层和3小层(L111,L112,L113),4小层(L114)相对较少.基于层次分析法的页岩气储层地质工程一体化甜点评价,可以将定性分析和定量分析相结合,为页岩气甜点的圈定提供了一种新思路,提高了甜点评价结果的合理性和准确性.

四川盆地埋深3500 m以浅五峰组—龙马溪组超压页岩气已实现规模效益开发,目前正在探索埋深3500～4000 m深层高温高压页岩气藏有效开发技术.Haynesville为北美典型高温高压深层页岩气藏,与国内中浅层开发区和深层探索区具备一定对比性,其开发技术政策可供参考借鉴.针对Haynesville页岩气藏2009-2019年4800 口完钻气井的钻井、压裂、生产和成本参数进行系统统计分析,研究表明不同埋深范围气井水平段长、用液强度、加砂强度逐年上升,段间距逐年缩小,百米段长EUR保持稳定.2019年,埋深3000～3500 m气井平均测深6306 m、水平段长2804 m、钻井周期35 d、段间距42 m、用液强度57.2 m3/m、加砂强度6.12 t/m、百米段长首年平均日产气1.01×104 m3/d、百米段长EUR为1038 × 104m3、单井总成本893×104 USD、建井周期159 d,单位钻压成本产气量30.7 m3/USD.埋深3500～4000 m气井平均测深6257 m,水平段长2385 m、钻井周期28 d、段间距42 m、用液强度51.1 m3/m、加砂强度5.77 t/m、百米段长首年平均日产气1.15×104 m3/d、百米段长EUR为1324×104 m3、单井总成本906×104 USD、建井周期201 d,单位钻压成本产气量34.8 m3/USD.随水平段长增加,百米段长EUR稳定在1000×104 m3～1300×104 m3,单位钻压成本产气量呈上升趋势,水平段长具备继续增加空间,第二年产量递减率由70.6％下降至目前50.6％.借鉴Haynesville页岩气藏开发实践,四川盆地埋深2000～3500 m以浅规模开发区可继续探索长水平段气井高产模式.3500 m以浅规模开发区和3500～4000 m深层探索区均可借鉴"控压"减缓产量递减方式提高气井最终可采储量.

Predicting the production behaviors of shale gas wells is of great importance for further developing future unconventional hydrocarbon strategies. An accurate prediction production, as well as reliable shale gas production models, are required to fully understand the shale gas exploitation budget. However, a major problem with classical analytic methods is the insufficient accuracy of the existing models, the time-consuming collection of historical production data, and the costly computational expense. To minimize this problem, a combination of the exponential smoothing method, autoregressive integrated moving average (ARIMA) model, and long short-term memory (LSTM) model was proposed to provide robust support for the production behaviors of shale gas. In this paper, we employed shale gas well production data to establish a database for model training and optimized the predicted model. Hereby, we sought to evaluate the production data predicted by conventional analytical methods, the exponential smoothing method, the ARIMA model, and the LSTM model. Shortly afterward, we objectively compared the predicted results obtained by the novel LSTM model and traditional analytical methods, such as Arps, stretched exponential decline (SEPD), and the Duong model. Herein, we compared the computational cost between the LSTM model and traditional numerical simulation. The combined interpretation of the proposed model demonstrates that the LSTM model achieved scientific accuracy and outstanding results in both short-term and long-term predictions, and realized production prediction of the adjacent well, with excellent agreement with the real shale gas production and a low error, making it an effective tool in forecasting shale gas production. This assay could be used as a potential approach for evaluating deep learning in the petroleum industry and for predicting the future production of unconventional hydrocarbons.

我国四川盆地埋深3500 m以浅五峰组—龙马溪组超压页岩气已实现规模效益开发,目前正在探索埋深3500～4500 m深层页岩气有效开发技术.Eagle Ford为北美新兴深层页岩油气藏,与国内中深层开发区和深层探索区具备一定对比性,其开发技术政策及学习曲线可供参考借鉴.依托页岩气云数据智慧平台对2009—2019年Eagle Ford页岩油气藏干气产区6223口水平井的钻井、压裂、生产和成本参数进行系统分析.研究显示,Eagle Ford干气产区中深层开发效果大幅优于深层.目前中深层气井平均测深6013 m,水平段长2558 m,钻井周期25.3 d,平均段间距50.0 m,加砂强度3.81 t/m,用液强度24.2 m3/m,百米段长最终可采储量(EUR)为677×104 m3,单井钻压成本617万美元,百米水平段长压裂成本13.4万美元;深层气井平均测深6394 m,水平段长2423 m,钻井周期33.3 d,平均段间距50.0 m,加砂强度4.03 t/m,用液强度26.9 m3/m,百米段长EUR为520×104 m3,单井钻压成本697万美元,百米水平段长压裂成本16.1万美元.Eagle Ford干气产区工程组织施工效率高,水平井建井周期主要为100～150 d,目前建井周期100 d;不同水平段长对应单位钻压成本产气量呈三角形分布,中深层气井合理水平段长2300 m,深层气井合理水平段长1600 m.我国中深层成熟开发区应探索合理水平段长实现效益最大化,深层探索区初期应适当控制水平段长.

The flow of shale gas in nano scale pores is affected by multiple physical phenomena. At present, the influence of multiple physical phenomena on the transport mechanism of gas in nano-pores is not clear, and a unified mathematical model to describe these multiple physical phenomena is still not available. In this paper, an apparent permeability model was established, after comprehensively considering three gas flow mechanisms in shale matrix organic pores, including viscous slippage Flow, Knudsen diffusion and surface diffusion of adsorbed gas, and real gas effect and confinement effect, and at the same time considering the effects of matrix shrinkage, stress sensitivity, adsorption layer thinning, confinement effect and real gas effect on pore radius. The contribution of three flow mechanisms to apparent permeability under different pore pressure and pore size is analyzed. The effects of adsorption layer thinning, stress sensitivity, matrix shrinkage effect, real gas effect and confinement effect on apparent permeability were also systematically analyzed. The results show that the apparent permeability first decreases and then increases with the decrease of pore pressure. With the decrease of pore pressure, matrix shrinkage, Knudsen diffusion, slippage effect and surface diffusion effect increase gradually. These four effects will not only make up for the permeability loss caused by stress sensitivity and adsorption layer, but also significantly increase the permeability. With the decrease of pore radius, the contribution of slippage flow decreases, and the contributions of Knudsen diffusion and surface diffusion increase gradually. With the decrease of pore radius and the increase of pore pressure, the influence of real gas effect and confinement effect on permeability increases significantly. Considering real gas and confinement effect, the apparent permeability of pores with radius of 5 nm is increased by 13.2%, and the apparent permeability of pores with radius of 1 nm is increased by 61.3%. The apparent permeability model obtained in this paper can provide a theoretical basis for more accurate measurement of permeability of shale matrix and accurate evaluation of productivity of shale gas horizontal wells.

Three-dimensional (3D) geological property modeling is used to quantitatively characterize various geological attributes in 3D space based on geostatistics with the help of computer visualization technology, and the results are often stored in grid data. The 3D geological property modeling includes two main components, grid model generation and property interpolation. In this review article, the existing grid generation methods are systematically investigated, and both traditional and multiple-point geostatistical algorithms involved in interpolation methods are comprehensively analyzed. It is shown that considering the numerical simulation of oil reservoirs, the orthogonal hexahedral grid remains the most suitable grid model for simulations in petroleum exploration and development. For the interpolation methods aspect, most geological phenomena are nonstationary, to simulate various types of reservoirs; the main development trends are increasing geological constraints and reducing the limitation of stationarity. Both methods have certain constraints, and the multiscale problem of multiple-point geostatistics poses a main challenge to the field. In addition, the deep-learning based method is a new trend in geological property modeling.

Low-permeability reservoirs are important to the future growth of oil and gas reserves and production in China. Predicting the effective stress, σe, in reservoirs is vitally important due to its considerable impact on reservoir development through hydraulic fracturing. This paper presents methods for predicting the σe field in ultralow-permeability reservoirs through reservoir–geomechanics coupling, which involve the simulation and coupling of the tectonic stress σ and pore pressure Pp fields based on three-dimensional (3D) geological models. First, 3D geological models were constructed based on basic data for the oilfield where the reservoir of interest is located. Then, finite element and finite difference simulations were performed to construct the σ and Pp fields, respectively, in the reservoir. Different types of initial σe were coupled based on 3D geological models. Subsequently, a dynamic σe field in the reservoir was established based on oilfield production data in conjunction with the transformation, optimization, and coupling of specific grid property parameters obtained from different numerical methods. Finally, the proposed methods were tested on real-world data acquired from well area X in an oilfield in Shaanxi Province, China. The results show that the proposed methods can be used to establish the σ and Pp fields in a reservoir based on 3D geological models combined with different numerical methods, and subsequently predict the σe value in the reservoir.

In recent years, big data and artificial intelligence technology have developed rapidly and are now widely used in fields of geophysics, well logging, and well test analysis in the exploration and development of oil and gas. The development of shale gas requires a large number of production wells, so big data and artificial intelligence technology have inherent advantages for evaluating the productivity of gas wells and analyzing the influencing factors for a whole development block. To this end, this paper combines the BP neural network algorithm with random probability analysis to establish a big data method for analyzing the influencing factors on the productivity of shale gas wells, using artificial intelligence and in-depth extraction of relevant information to reduce the unstable results from single-factor statistical analysis and the BP neural network. We have modeled and analyzed our model with a large amount of data. Under standard well conditions, the influences of geological and engineering factors on the productivity of a gas well can be converted to the same scale for comparison. This can more intuitively and quantitatively reflect the influences of different factors on gas well productivity. Taking 100 production wells in the Changning shale gas block as a case, random BP neural network analysis shows that maximum EUR can be obtained when a horizontal shale gas well has a fracture coefficient of 1.6, Type I reservoir of 18 m thick, optimal horizontal section of 1600 m long, and 20 fractured sections.