It is generally known that shale layers are the source rock layers of hydrocarbons. The shale gas is a kind of methane gas which is adsorbed on the surface of shale particles. The weight-volumetric method is used to estimate the geological resources of shale gas. Due to the extremely low permeability of the shale, it is represented by the nano Darcy levels （1 nD=10^-9 D=10^-6 mD）, and the adsorbed gas is in a saturated adsorption state. Therefore, the development of shale adsorbed gas requires horizontal well drilling, multi-stage fracturing, depressurization and desorption before putting into production. Since the horizontal well is an independent closed production unit for the shale gas fields, it is necessary to estimate the well-controlled recoverable reserves firstly, and then to estimate the recoverable reserves of the shale gas field. Based on the normalized production decline curve shifting method proposed by Baihly, the well-controlled recoverable reserves and the reserves of the estimation areas from the three large shale gas fields （Barnett, Woodfort, and Haynesville） in the United States have been effectively predicted using the generic exponential production decline（GEPD） model proposed by Chen Yuanqian in recent years. Furthermore, the production decline rate of shale gas wells is theoretically analyzed, and the results completely accord with the actual production decline characteristics of shale gas wells. It shows that the production of a gas well decreases rapidly in the initial stage, and gradually slows down after another 20 months of production. At the same time, five kinds of decline models are studied and estimated in this paper.

Shale gas resource evaluation includes resource calculation, favorable distribution area and economic effectiveness based on geological and exploration process analysis. Its core is evaluation method selection, parameter processing and result analysis in line with geological process evolution characteristics and data mastery degree. The intelligent evaluation of shale gas resources can overcome the limitations of real resource evaluation, and can realize the whole process simulation and evaluation from qualitative to quantitative. It has obvious characteristics of development stages. The main feature of resource evaluation at this stage is to use modern means such as machine learning and inference engine. Method selection, parameter quality and evaluation effect are the keys to shale gas resource evaluation. Knowledge base establishment based on geological characteristics and exploration level, data collection, parameter analysis, data mining, geological reasoning, method selection, intelligent calculation, and reliability of results analysis, spatial expression of results and continuous execution throughout the process are the basic ideas and methods for intelligent evaluation of shale gas resources. Intelligent evaluation with powerful functions and continuous implementation throughout the whole process is the basic direction of the development of shale gas resource evaluation, which requires continuous accumulation and practice on the basis of existing technologies to promote the development of shale gas resource evaluation methods and technologies in a wider range.

Due to the multi-stage tectonic movement, the geological conditions of shale in Nanchuan are complex and resource endowment is deteriorating, which makes it difficult to realize the scale economy development. In order to actively promote the beneficial development of Nanchuan shale gas, it insists on the development concept of geological engineering integration and all-around whole-process optimization, and carries out comprehensive research on “research deployment, well pattern optimization, drilling engineering and fracturing engineering” and other aspects. According to the principle of fully utilizing underground resources and optimizing before surface drilling, the optimal deployment of drilling platform and well pattern is determined. The well placement design is optimized with productivity sweet spot as the core, meanwhile, the integrated steering technology is applied to achieve precise penetration and improve sweet spot penetration. Aiming at maximizing the complexity of fracture network and productivity, the fracturing process design is optimized to maximize the benefits of shale gas wells. Based on the production, construction and implementation of shale gas in Nanchuan area, an integrated model of the whole process of “research, deployment, design, implementation and support” has been formed under the characteristics of Nanchuan complex structural belt, which can improve the drilling speed and fracturing production, and promote the beneficial development of shale gas in Nanchuan area.

Shale oil reservoir bears heterogeneous pore structure with multi-scale pore sizes. Nano-scale organic intra-granular pore, nano-micro scale inorganic inter-granular pore and micro-fracture coexist in shale oil reservoir. The ultra-low porosity and ultra-low permeability characteristics make the laboratory core flow experiment unavailable for shale oil core sample. As a consequence, shale oil flow parameters in different scale of porous medium can not be measured and it is difficult to accurately evaluate the shale oil flow ability in different scale of porous medium. To solve this problem, a calculation method for pore structure characterization and flow ability of shale oil reservoir is proposed based on digital cores. The nanopore shale oil flow model is first established considering nano-micro scale transport mechanisms and occurrence state, and the influences of shale pore surface physicochemical property and pore size on shale oil flow are analyzed. Then, the nanopore shale oil flow model is further extended to 3D porous media by establishing pore network shale oil flow model. The digital cores in different medium and its pore network are constructed based on the multi-scale shale core imaging data in shale oil reservoir. The multi scale pore structure characteristic and shale oil flow ability are studied in detail on this basis. The analysis results indicate that when the pore radii are less than 5 nm, the shale oil permeability is dependent on adsorbed phase permeability. Inter-granular pore dominates shale oil flow ability. The micro-scale effect on shale oil permeability is very small which can be neglected. The micro scale effect and oil slippage are more obvious in organic pores. However, the contribution of organic pore permeability on total shale oil permeability relies on the connectivity of organic pore structure.

Taking the source rock of Chang 7 Member in Ansai area of Ordos Basin as the research object, the differentiation characteristics of shale oil in the process of migration and accumulation are discussed in depth through a series of testing methods, such as TOC test, rock pyrolysis and chromatography-mass spectrometry. The results show that the hydrocarbon generation potential of different source rocks in the study area is quite different. The shale has the strongest hydrocarbon generation capacity, while that of the interaction layer of sandstone and mudstone is the weakest. The high content of free hydrocarbon in interaction layer of sandstone and mudstone results from the receipt of external free hydrocarbon. The distribution of geochemical parameters of shale oil in different lithology is different. Among them, the sand mud interaction layer is rich in saturated hydrocarbon but relatively poor in nonhydrocarbon and asphaltene, while the shale and the mudstone are rich in nonhydrocarbon and asphaltene but relatively poor in saturated hydrocarbon. The properties of crude oil, thickness of source reservoir and physical properties have effects on shale oil migration and accumulation. Shale oil in thin-layer source rocks and small molecular hydrocarbon components are easy to migrate. Shale oil in thick-layer source rocks and macromolecular hydrocarbon components tend to remain in the source rocks.

The southeastern Sichuan region has large elevation differences, complex surface conditions, large stratum dip, and complex conditions underground, so that the accurate imaging is difficult. In the support of shale gas exploration and development, there are problems of inconsistent well seismic wave group occurrence, false faults and well seismic prediction errors for the pre-processed data in this area. Aiming at these problems, the well-control anisotropic pre-stack depth migration method is proposed and applied to the above three-dimensional work area. By improving the accuracy of the velocity model, the seismic imaging accuracy of the complex fold area is improved, and it plays an important role in guiding the later well position, and improving the high-quality shale drilling rate. In this paper, the accuracy of the migration velocity model is finely characterized from two aspects: isotropy and anisotropy. Through the deep research and practice of high-precision grid tomography velocity modeling, travel（TPT）and TTI（transverse isotropic media with axial inclined symmetry）, the key technology series of seismic precision imaging in southeast Sichuan is gradually improved to enhance the well seismic yield consistency, reduce the well earthquake depth prediction error, eliminate the fault illusion of seismic profile, and provide important technical support for subsequent exploration and development of shale gas deployment.

At present, taking a single well as an evaluation unit is a common and accurate method to estimate SEC reserves of shale gas. With the further development of the gas fields, the single well production fluctuates greatly, which can not better reflect the development performance of the gas reservoir. In order to further improve the accuracy of the estimation, Pingqiao South Block of Nanchuan Gas Field is divided into four zones, namely Zone Ⅰ, Zone Ⅱ, Zone Ⅲ and Zone Ⅳ, by the study of regional production rules and combining with geological characteristics based on the single well estimation. The yield normalization method is used to establish the typical curve of each zone, and then the theoretical yield model of this block is established by the dual medium model, so as to meet the harmonic decline. Finally, the evaluation models of different zones are established. Zone Ⅰ directly enters into the harmonic decline with an initial decline rate of 56.5 %; Zone Ⅱ enters into the harmonic decline after 23 months of stable production with an initial decline rate of 52.2 %; Zone Ⅲ enters into the harmonic decline after 13 months of stable production with an initial decline rate of 60.7 %; Zone Ⅳ directly entered into the harmonic decline with an initial decline rate of 59.5 %. The final estimation results are compared with those of foreign countries, and the error is within 6 %, which shows that the estimation of the SEC reserves for each unit of shale gas in Pingqiao South Block in the middle and later period of development has certain adaptability.

During drilling the horizontal section of a shale gas well, oil-based drilling fluid is commonly used in China in order to ensure downhole safety, especially to solve the problems of lost circulation and well collapse. Based on the study of chemical solidification plugging technology and the XRD（X-ray diffraction） analysis of Longmaxi shale samples in Chongqing area, the nano micron size plugging materials are selected to explore a new technology for plugging and collapse prevention of shale gas wells. Field application of chemical solidification technology effectively solves the lost circulation problem of a well in Jiaoye, and meet the requirements of subsequent construction. In the process of drilling, the polysilicon fiber and micro-nano plugging agent selected in the laboratory not only completely control the collapse and block falling, but also reduce the daily average drilling fluid consumption by 1/3, so as to achieve the purpose of anti-seepage and anti-collapse. It is proved that chemical solidification and micro-nano plugging are effective methods to solve the plugging and collapse prevention of shale gas wells, which play a positive role in the development of shale gas wells in Chongqing area.

The Permian Lucaogou Formation in Jimsar is rich in continental shale oil reserves. There are two “sweet spot” developed in the vertical direction. As the development progresses advances, the lower “sweet spot” becomes the main development layer. It has the characteristics of large depth and strong heterogeneity, which makes the development difficult. With the impact of market environmental pressure, it is urgent to optimize the stimulation technology to realize the low-cost development of shale oil. Based on the geological characteristics, the methods of numerical simulation, experiments, and fracture monitoring are used to study the cluster spacing, sand-carrying performance of slick water, conductivity of proppant and so on. Then, a series of low-cost tests such as intensive stage, temporary plugging fracturing, quartz sand replacement, and slick water sand injection have been carried out. The application effect showed that the comprehensively use of low-cost stimulation technology can reduce the cost by at least 30 %, meanwhile, maintain the output. It provides a reference for the subsequent development for Jimsar shale oil.

The distributed optical fiber temperature monitoring technology is gradually being used to monitor the downhole production of fractured horizontal wells. However, it is still a huge problem to interpret the production profile of fractured horizontal wells quantitatively based on DTS data in low permeability gas reservoirs. In view of this, this study established the method, first, the temperature data preprocessing, and then, on the basis of the principle of conservation of mass and energy conservation of horizontal wells in low permeability gas reservoir fracturing coupling temperature forward model, finally using a variety of traffic data of mathematical methods of inversion layers, forming a set of monitoring based on distributed optical fiber temperature measurement technology of horizontal wells in low permeability gas reservoir fracturing of output profile processing and interpretation methods.The actual data processing of fractured horizontal well 5 was carried out by using the established method.The results show that the forward temperature fitting curve is basically consistent with the original temperature curve, which indicates the rationality and accuracy of the forward model.In addition, the calculated absolute errors of daily gas production of the five wells were between 100 m³ and 1 712 m³, and the absolute errors of daily water production are between 0.7 m³ and 1.8 m³. The errors are small and meet the production requirements. All these provided technical support for the development of low permeability gas reservoirs.

In order to clarify the main principal factors that affect the initial productivity during the development of shale oil reservoirs, a comprehensive data analysis method involved both the hierarchical cluster analysis and the principal component analysis in data statistics is presented; and then the deta of the static formation parameters, fracturing operation parameters and the oil productivity of 51 wells in Block Li-151 are analyzed quantitatively. At first, the wells in the block are divided automatically into two types, Type A and Type B, by the hierarchical cluster analysis method. Then, a principal component analysis method is used to analyze the principal productivity factors for different types of wells. Analysis results show that, when the well shut-in time is less than 125 days, the oil production decline rate can be reduced effectively by the well shut-in measures; however, when it is greater than 125 days, the effect of well shut-in measures on oil production decline rate becomes negative. The production decline rate of Type A wells is highly negative with the amount of injected fracturing water; the main principal factors for the production decline rate of Type B wells are the moving liquid level and the porosity of shale matrix. The principal factors for the production rate of Type B wells are the number of fracturing sections. All in all, for the production optimization of shale oil development in Block Li-151, the differences of principal production factors between Type A wells and Type B wells should be considered and the different analysis results of the principal factors that affect the initial shale oil productivity under different well types should be fully utilized. Some guidance can be provided specifically for the formulation of a reasonable shale oil efficient development plan.

Nanchuan Block is rich in shale gas resources, and the proven reserves of Pingqiao and Dongsheng structural belts are nearly 2 000×10⁸ m³, mainly in normal pressure shale gas reservoir, with complex structure and stress, and relatively poor resource quality. Affected by the preservation conditions, burial depth, in-situ stress, complex structure, uneven fracture development and fracturing effect, the productivity of single wells on the same platform is quite different, and the test production, productivity and single well EUR of some wells have not been able to meet the expectation, all these above greatly limit the scale production and benefit development. By the comparative analysis of production effects of typical wells in geology, development and engineering, it is considered that formation pressure coefficient, development degree of natural fracture network, local complex structure and in-situ stress are the main factors affecting normal pressure shale gas development in resource implementation area. The pressure coefficient represents the driving energy of the formation, and a certain degree of natural fracture network can effectively improve the development effects. The local complex structure leads to a low drilling rate of high-quality shale, and the stress increase caused by extrusion deformation, deepening burial depth and excessive angle between horizontal section azimuth and minimum principal stress azimuth will limit the complexity of artificial fracture network to a certain extent. According to the plane difference distribution characteristics of main controlling factors, the basis of improving the development effects of normal pressure shale gas is to select the best from sweet spot and adjust the local optimization.

Influenced by the underwater paleouplift and multi-stage structural extrusion of Leshan-Longnyusi, Yuxi Block gradually deepens from north to south, and has the characteristics of large thickness change of type I reservoir and low porosity（less than 4.5 %）, it is very important to study the pore structure of high quality shale. Traditional shale pore analysis technology has weak identification ability for different types and sizes of micro nano pores. Through an optimized analysis method based on maps image, it can reduce the space-time complexity of the algorithm and improve the applicability on the premise of effectively identifying and statistics the organic and inorganic pore fractures of shale. Characterization of organic pore fractures in Longmaxi formation is about 300 μm, inorganic pore seam with a visual field of view length of 500 μm. The diameter or opening range of organic pore gap detected by maps image is mostly between 0~100 nm. The organic matter content and organic matter porosity are different, and the abundance of organic matter is not the only control factor for the development of organic matter pores.

Reservoir permeability is an important factor affecting reservoir productivity. In order to solve the problem of poor prediction accuracy of conventional permeability logging models in low-permeability sandstone reservoirs with poor pore connectivity, a scheme combined with deep belief network（DBN） algorithm is proposed. First, the gray correlation method is used for the correlation analysis of logging curve, and according to the correlation ranking, the characteristic sensitive curves is sorted. Then, the optimization by supervised learning is combined with the contrastive divergence for the data mining to establish the prediction model of permeability. Compared to the previous BP neural network, DBN model improves the local optimization, and enhances the training efficiency and prediction accuracy. The average relative error of the prediction model is 9.1 %, which is about 20 % lower than that of the conventional permeability model. Based on the actual data processing applications and the error analysis, it is found that this method can effectively improve the prediction accuracy of permeability for the low permeability reservoirs.

The machine learning method is the main technical means of carbonate lithofacies log identification. Selecting the appropriate machine learning method according to the different geological conditions and data is one of the key factors for high-precision identification of lithofacies. However, there are few researches on the applicability of machine learning identification methods. In this paper, four most commonly used machine learning methods for identifying lithofacies are studied, including Self Organizing Maps（SOM）, Multi-Resolution Graph-based Clustering（MRGC）, K Nearest Neighbor（KNN）, and Artificial Neural Network（ANN）. By comparing the principle and practical application effects of these methods, the advantages, disadvantages and applicability of the four machine learning methods have been summarized. When there are few core samples, MRGC is preferred, while when there are more core data, KNN is preferred as well as MRGC. Their application of lithofacies identification in the Longwangmiao Formation in the MX area in Sichuan Basin shows that MRGC and KNN are the best, SOM is the second, and ANN is the worst. This study of the application effects of machine learning methods provides a guidance for the identification of carbonate rock facies in other layers and regions, and has strong practical value.

The analytical method and other qualitative research methods to guide the field leakage prevention and plugging lack of pertinence. Meanwhile, the engineering data used by the quantitative prediction method in the prediction of leakage is incomplete and how to control the leakage quantitatively by adjusting the relevant parameters is not proposed after the loss degree is predicted. In order to solve the above problems, the cocoon stripping algorithm is proposed. Selecting all measurement parameters of 27 of 29 wells in Shunbei Oilfield with average loss rate recorded, and then taking the average loss rate as the objective function, the well depth and drilling and completion fluid density as the independent variables, the multiple linear regression equation is established, and the undetermined coefficients of the equation are solved. Theoretically, T test and F test are used to prove that the equation can meet the requirements of engineering analysis. In practice, the relative error between the calculated value and the actual value of two wells that did not participate in the equation establishment is about 6 %, proving that the equation meets the needs of engineering control. Finally, 17 factors affecting the average loss rate have been screened out by the target equation simplified by the contribution rate method and the cutting element method, among which 10 drilling fluid factors contribute more than 50 %, indicating that a certain average loss rate could be controlled by adjusting the performance of drilling fluid. For the convenience of field control, the performance of drilling fluid is changed by the reading of six-speed viscometer at 300 r/min, so that the main controlling parameters of drilling fluid are reduced to two, which are pH value and the reading of six-speed viscometer at 300 r/min. The minimum average loss is predicted to be 2.3 m³/h through the analysis of the three-element equation. Considering the characteristics of Shunbei block, the leakage in Shunbei oilfield involves geological, engineering, and other operation links is found out through quantitative analysis, and so does the main controlling factors of drilling and completion leakage. By means of cutting elements, contribution rate and other methods, the minimum average leakage rate obtained by controlling the funnel viscosity and the reading of six-speed viscometer at 300 r/min is calculated. In the aspect of guiding the field practice of plugging leakage, it can provide basic data support for the subsequent construction measures to obtain the ideal leak control effect, and provide an optional means for decision-making.

The epidermal coefficient is mostly used to describe the reservoir damage degree of drilling fluid, but cannot quantitatively characterize the relation between the specific performance index and the reservoir damage degree, and effectively guide the optimization and adjustment of field drilling fluid performance. However, for the big data method, it has obvious advantages in multi-factor analysis. Based on this, a multi-parameter drilling fluid reservoir injury model is established to realize the working fluid optimization to protect the reservoir. To this end, the on-site data of nine completion wells withe fuzzy ball drilling fluid and six other adjacent completion wells are collected. The average daily output difference is took as the target function, with seven parameters of drilling fluid density, apparent viscosity, plastic viscosity, funnel viscosity, dynamic plastic ratio, dynamic shear force, and pH value as the independent variables. Firstly, the multiple regression method is used to establish a multi-parameter model. Then the mathematical model of drilling fluid on reservoir damage is established by the main controlling factors found by cocoon stripping algorithm. Finally, the quantitative relationship between the fluid performance and the average daily yield is defined. The study found that the regression coefficients of apparent viscosity, density, dynamic plastic ratio, and pH value are -1.561, 0.428, -0.535, 1.60, respectively, indicating that the apparent viscosity of fuzzy ball drilling fluid caused greater damage to the reservoir, density, dynamic plastic ratio, while the density and pH value have the protection effect of reservoir. In the case of the adjustment of drilling fluid performance of the horizontal section in Well-Yan5-V1 guided by the regression model, the average daily production after commissioning is increased by nearly 800 m³. In conclusion, compared with the on-site evaluation methods such as well testing, the big data method can only accurately diagnose the damage degree but also provide theoretical basis for the optimization of site drilling fluid performance. Meanwhile, it also provide a method for evaluating reservoir damage, and the application effect on site is obvious.

The machine learning is not only an important tool for oil and gas big data analysis, but also a general data-driven analysis method. As an important field with a long history and a large data base, oil and gas exploration and development has a great potential for data mining. The use of big data analysis technology for oil and gas field can help decision makers to conduct investment analysis, risk assessment and production optimization, which brings significant economic benefits. The machine learning method has been tried by the researchers applying to the researches on oil and gas. Nowadays, many application scenarios have been proposed with the development of machine learning algorithms, but general solutions for specific scenario are still divided. So that, we introduces the procedure of a machine learning modeling upon the most basic principles, and summarizes the development history of the main three kinds of machine learning methods that can be applied to oil and gas big data analysis. And then based on the characteristics of oil and gas field big data, the core contents, goals and advantages of oil and gas field big data analysis and utilization are discussed, the main application scenarios of machine learning in oil and gas field are analyzed, and the existing problems and countermeasures in typical oil and gas production prediction are summarized.

The successful development of shale gas has effectively promoted the optimization of China’s energy structure. However, in terms of shale gas well production rules, reasonable production systems and on-site management, the traditional manual and computer operating mode can no longer meet the needs of simultaneous tracking analysis and fine gas reservoir management. Meanwhile, there is no mature theory, technology and experience for reference at home and abroad. In order to achieve rapid response for abnormal situation and efficient management of gas reservoirs, the development of the production auxiliary decision-making system has been initiated simultaneously during the development of Fuling Shale Gas Field. The early warning research has been carried out from the direction of low pressure, liquid loading, production system changes, etc., and the short-term, mid-term and long-term analysis and the prediction of production decreasing trend of a single well have been carried out either. The management goal to obtain useful information by data mining for decision-making promotion is realized. This system effectively supports the on-site management of the shale gas field and ensured the continued stable production of the shale gas wells in Fuling Shale Gas Field. The intelligent construction and application of shale gas fields pioneered in Fuling Shale Gas Field has laid a good foundation for the refined management of shale gas reservoirs in China, and has important reference value and application prospects.

There are many nodes in the comprehensive research process of exploration and development, which are highly professional. In order to solve the problems that the research process is not explicit, and lack of data service support and visualization means, a standardized comprehensive research process of exploration and development has been formed, and a digital platform for comprehensive research of oil and gas exploration and development has been developed, so as to achieve rapid acquisition of research data and automatic archiving of research results. Integrating the professional software data service and visual analysis support means, and calling the required professional software with one click can realize multi-disciplinary online collaboration of different research posts. Through the standardized comprehensive research process, the new staff can be able to enter into the spirit of the role as soon as possible, and the valuable experience of the old staff can be inherited through the continuous improvement of the process. The platform has been widely applied in the main research office of Shengli Oilfield Exploration and Development Research Institute, supporting the exploration comprehensive research of seven seismic work areas, and the scheme preparation and optimization of 15 new and old areas, greatly improving the efficiency and quality of comprehensive research work, and providing support for efficient exploration and beneficial development of the oilfield.

Carbon capture, utilization and storage, or CCUS, is one of the key technologies to deal with global climate change, among which CO₂-EOR is an important part. In order to effectively solve the problems that caused by the high cost and low oil price on CO₂ flooding, and promote the large-scale popularization and application of CCUS project, Jiangsu Huayang Liquid Carbon Co., LTD. of Sinopec East China Petroleum Bureau strengthen the integration of the advanced information of carbon dioxide transportation or oil displacement and the production management according to the template of digital oil and gas field construction and in accordance with the working idea of “production automation, safety interlock, unmanned on site and centralized management”. Through the construction and operation of safety monitoring system and information remote control platform, the digital management of CO₂ transportation system, injection system and production area security system is realized, which ensure the safety standard of carbon dioxide transportation, intelligent and efficient oil displacement site and effective supervision in production area. The exploration and practice of digital management transformation of distributed CO₂-EOR project have been completed. By this systematic digital construction, the lowest comprehensive cost and the best security guarantee of CO₂-EOR has been achieved, which is of great significance to the large-scale popularization and application of CCUS project.

The southern Yanchuan Block is a deep, high-rank coal-bed methane reservoir with large buried depth at 800~1 500 m, meagre coal or anthracite, and good gas content at 12 m³/t. The resource conditions are relatively favorable, but the single well productivity level of the gas field varies greatly. Based on the comprehensive study of gas field geological conditions, and by the analysis of the influence of structure on reservoir hydrocarbon generation, porosity and permeability, hydrogeological conditions, coal body structure, and CBM storage, the researches on the control mechanism of structure on CBM reservoir formation are carried out to find out the main controlling factors of coal seam formation and its changing rules. Combined with the CBM development dynamic data, the relation between block structure and CBM well productivity is analyzed, and a structural gas control mode for CBM well productivity is established. The research results show that the buried depth of the coal seam controls the gas-bearing and permeability of the reservoir. The deeper the buried, the better the gas-bearing and the worse the permeability. The control effect of tectonic activities on the formation of coal-bed methane has two sides, the local folds and the development of faults has a significant effect on improving permeability, which is conducive to the enrichment of gas reservoirs, but the excessive tectonic activity on the other hand will also lead to the enhanced hydrodynamic conditions and the escape of coalbed methane. The structure has an obvious control effect on the gas well productivity. The high-yield wells are mainly distributed in the coalbed methane enrichment and the permeability improvement areas at the wing of the local micro-uplift belt with a buried depth of 830~1 280 m. The middle-production wells are mainly distributed in the low-permeability areas with a buried depth of more than 1 280 m, whose structure is gentle and the faults are not developed. The low-yield wells are mainly distributed in local depression areas with severe structural damage and CBM escape areas near fault development belts.

The reef gas reservoir of Changxing Formation in Yuanba Gas Field is an ultra-deep gas reservoir with bottom water and high sulfur content, and it is in the early stage of stable production. The dynamic monitoring shows that the risk of water invasion in the development of gas reservoir is high, which brings great challenges to the dynamic analysis of gas reservoir. Combined with the actual geological characteristics and production status of Yuanba Gas Field, the daily production and pressure data are fully used to carry out the application analysis of the yield instability analysis method （Agarwal Gardner, Blasingame, NPI, etc.）. The results show that this method can be applied to analyze the reservoir dynamic parameters, gas well dynamic productivity, dynamic reserves and water invasion identification of the gas reservoir in Changxing Formation Yuanba Gas Field. The obtained results are highly reliable, which is helpful to deepen the understanding of the geological characteristics of gas reservoirs, solve the production problems, and further guide the dynamic analysis of gas reservoirs.