Tight Gas Reservoirs Research Articles

Robotics and artificial intelligence in unconventional reservoirs: Enhancing efficiency and reducing environmental impact.

Shale, tight gas, and coal bed methane reservoirs are unconventional reservoirs that have geometrically complicated geological formations with low permeability, rendering their extraction operations difficult, expensive, and environmentally sensitive. On the other hand, recent developments in Robotics and Artificial Intelligence continue to transform how exploration, production, and maintenance activities are carried out in these reservoirs. This review paper encompasses how Robotics and AI technologies are applied to these unconventional reservoirs, improving operation efficiency, increasing the recovery rate and minimizing environmental damage. It updates on the latest status of autonomous drilling, AI-driven reservoir characterization and robotic fracturing. The paper also discusses future opportunities: emerging technologies, integration of AI with predictive analytics, and innovations toward sustainability. These are the challenges that come with possible solutions discussed in the paper: high costs, technical integration, and data quality. The review thereby presents the thought that robotics and AI will take a transformative role in the unconventional reservoir sector during the next few years, both in economic performance and environmental sustainability.

Calculation and application of elastic modulus of mineral components in tight sandstone based on adaptive method

Abstract For the petrophysics modelling of tight sandstones, the elastic modulus of their sandstone and mudstone components are often substituted with those of quartz and clay, which affects model accuracy. To solve this problem, we innovate an adaptive approach for modelling the rock physics characteristics of tight sandstone. First, based on the relationship between P- and S-wave velocities from well logs and the elastic modulus of the rocks, the equivalent elastic modulus of tight sandstone under saturated conditions is calculated. Next, The Lee model and the Gassmann equation were jointly used to determine the equivalent elasticity modulus of tight sandstone matrix. The upper and lower limits of the equivalent elasticity modulus for mudstone and sandstone are established using the mudstone content curve, the least squares method and the Voigt–Reuss–Hill (V-R-H) model. We used the random-walk algorithm to accurately calculate the equivalent elastic modulus of the sandstone and mudstone components. Finally, using the accurately obtained elastic modulus of sandstone and mudstone, the equivalent elastic modulus of the matrix is calculated. The Kuster–Toksöz (K-T) model is subsequently applied to compute the dry frame bulk modulus and shear modulus of the rock. Following this, the Brie model is used to calculate the elastic modulus of the mixed fluid, thereby completing the construction of the rock physics model for the study area. The results demonstrate that our improved petrophysics model can predict the S-wave velocity curve with ≤ 15% errors relative to the true curve. When sweet spot prediction was performed using reservoir-sensitive parameter (Vp/Vs) derived from the petrophysics template, the agreement between the predictions and well-log data was 80%. Thus, our petrophysics modelling method can be used to predict tight gas reservoirs effectively and will aid efforts to improve petroleum exploration works in the study area.

Current Status of Research on Seepage Mechanism in Tight Sandstone Gas Reservoirs

China's tight sandstone gas reservoirs have a large amount of resources, which is an important part of future energy. At present, one of the main problems facing the production of tight sandstone gas reservoirs is water production, and the water production of gas wells is a key influence factor restricting the stable and increased production of gas reservoirs. Firstly, the research status of reservoir characteristics of tight sandstone gas reservoirs is summarized, then the seepage mechanism of tight sandstone gas reservoirs is analyzed, and finally the relationship between gas and water in tight sandstone gas reservoirs is reviewed, which provides a basis for further research on the seepage mechanism of tight sandstone gas reservoirs and the law of gas and water production.

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

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

Micro-scale flow simulation study of low-yield wells in tight gas reservoirs

Abstract The mechanism of gas-water flow in reservoirs has traditionally been characterized macroscopically based on flow experiments, but the microscopic mechanisms of gas-water flow in porous media cannot be precisely described experimentally. This paper categorizes low-yield gas wells into Types I, II, and III. With microscopic visualization principles, three different types of core thin-section photos were selected to construct a pore structure model, allowing for micro-scale flow simulation to examine micro-scale percolation patterns in various types of low-yield gas well reservoirs. The results show that there is a shorter displacement time in Type I reservoirs compared to Type II and III reservoirs which exhibit more pronounced fingering phenomena.

Estimating pore pressure in tight sandstone gas reservoirs: A comprehensive approach integrating rock physics models and deep neural networks

Pore pressure serves as an important driving power for subsurface fluid migration and therefore has a significant impact on gas accumulation and enrichment in tight sandstone reservoirs. Tight gas is typically produced in overpressure regions, where pressure coefficients are notably elevated. Thus, it is crucial to establish an effective methodology for precise pore pressure estimation. This study introduces an approach to improve pore pressure prediction by incorporating rock physical modeling and deep neural networks (DNNs) into the classical Eaton method. Compared to conventional techniques relying on empirical correlations between pressure coefficients and elastic properties, the proposed method considers the influence of porosity, fluids, and lithology, which could enhance reliability in pore pressure prediction. Meanwhile, a prediction model is developed using logging data and DNNs to estimate mineralogical volumetric fractions based on elastic properties. This prediction model allows improved estimation of rock matrix elastic properties using seismic-inverted data, which is crucial for estimating normal compaction velocity to extend pore pressure prediction from individual boreholes to the whole study area. Real data applications demonstrate that the predicted pressure coefficients derived from seismic data using the method presented in this paper align well with the gas enrichment estimated in previous studies for the tight sandstone reservoirs. Furthermore, regions with high values of pressure coefficients correspond to high gas content. These findings validate the effectiveness of the proposed methodology, which can provide valuable insights for identifying potential tight sandstone reservoirs.

A data-driven dual-optimization hybrid machine learning model for predicting carbon dioxide trapping efficiency in saline aquifers: Application in carbon capture and storage

Geological carbon sequestration (GCS) is a promising method to reduce global carbon emissions. To better understand the CO2 trapping mechanism, the CO2 sequestration process was simulated through a physics-based reservoir simulator, and the residual trapping (RTI) and solubility trapping (STI) efficiencies were analyzed. Through the experiments, a database containing 4543 data points with reservoir physical parameters and rock properties was established. In this study, a kernel extreme learning machine (KELM) model based on variational mode decomposition (VMD) and dung beetle optimization optimizer (DBO) was proposed to perform the prediction task. Through the VMD, the original data sequences were decomposed and combined, and the obtained subsequences were executed modeling prediction and reconstruction respectively. The reliability of the prediction model was confirmed to be greater than 97.71% through domain analysis. In addition, it was applied to a tight gas reservoir in China in to confirm its validity in field applications. Comparative results indicated a high agreement between the prediction results and the simulation results, suggesting that the proposed method can complement numerical simulations and help users understand the application of machine learning in carbon capture and storage (CCS).

Refractured Well Hydraulic Fractures Optimization in Tight Sandstone Gas Reservoirs: Application in Linxing Gas Field

Poorly producing wells in sandstone gas reservoirs are often refractured to enhance production. Considering the mutual interference of initial/refractured fractures, conductivity dynamic evolution, non-uniform inflow, and variable mass flow in the fracture comprehensively, a semi-analytical reservoir-fracture coupled production model fusing spatial and time separation methods is introduced to model refractured well performance. The proposed model is verified by CMG. The field applications indicate that the refracture job should be carried out when production is lower than the desired value. Restoring the Cf-ini and constructing the Cf-ref can increase productivity, which increases over 8 D•cm. The production growth rate just obtained a slight improvement. The production increased significantly with Lf-ini increasing from 120~270 m and Lf-ref increasing from 100~150 m. Hence, it is essential to extend the Lf-ini under engineering conditions. The ks/km = 10 can obviously increase production, but further enlarging ks does not contribute to well performance. Conversely, further producing larger bs is vital to enhancing production. Subsequently, the optimal parameter combinations (ds > Lf-ini > Lf-ref > Cf-ini > ks > Cf-ref) for well(X1) are carried out by orthogonal experiments. This work proposes a novel method to simulate refractured vertical well performance in tight gas reservoirs for refracture optimization.

A general framework to optimize fracture parameters in tight gas reservoir of heterogeneous channel sand bodies

Optimizing fracture parameters can unlock the potential of tight gas reservoirs. Various frameworks have been proposed as efficient optimization tools. However, current frameworks are mostly aimed at regular rectangular oil or gas reservoirs. The effect of heterogenous channel sand bodies on fracture parameter optimization has not been discussed. Here, we proposed a general optimization framework. The first step is to establish a heterogeneous channel sand bodies model based on image processing and data interpolation methods. The second step is to couple optimization algorithm and reservoir simulator to optimize fracture parameters. Then the framework is applied to two actual wells in Shaximiao Formation in Sichuan Basin in China. Results show that, the effect of channel sand body on the fracture optimization cannot been ignored. The optimal fractures tend to be concentrated in areas with good physical properties and large control areas. The sand body boundary has a significant control effect on the fracture half-length. For both fracture length and conductivity, double dumbbell shape is better than uniform shape. Our general framework serves as an efficient tool to optimize fracture design in heterogeneous channel sand body, which plays a growing role in enhancing tight gas recovery.

Layered massive fracturing of carrier beds commercially unveils the deep tight sandstone gas reserves in Sichuan Basin

Achieving commercial production from deep tight gas formations poses challenges to existing geological and engineering technologies. Xujiahe tight sandstone gas, deeply buried in the Western Sichuan Basin, is proven with enormous reserves; however, preliminary hydraulic fracturing trials mostly failed to bring up the productivity. From the angle of incorporating geological and engineering knowhow, we first adopted the carrier beds theory to distinguish the favorable zones in Xujiahe Formation, then a dual sweet spot identification criterion was established based upon analytic hierarchy process, which links major geological and engineering parameters together. Provided the identified sweet spot, hydraulic fracture conductivity was then optimized to maximize the productivity. Fracturing operational parameters consisting of fluid volume and pumping, proppant size and addition, and fluid formula were then screened to secure high conductivity. Implementation of this updated geological-engineering technical route in exploiting Xujiahe tight gas reservoirs turns out to be a great success with an average gas rate of 130 Mm3/d/well during the early production stage in 14 wells. Electromagnetic monitoring further demonstrated the effectiveness of precise massive fracturing by visualizing the morphology of fracture clusters. Comparative productivity analyses manifested the breakthrough of this technical route over preceding ones in Xuejiahe gas reservoirs, providing a superior reference for discovering and developing deep tight gas resources around the world.

Evaluation of Fracturing Effect of Tight Reservoirs Based on Deep Learning.

The utilization of hydraulic fracturing technology is indispensable for unlocking the potential of tight oil and gas reservoirs. Understanding and accurately evaluating the impact of fracturing is pivotal in maximizing oil and gas production and optimizing wellbore performance. Currently, evaluation methods based on acoustic logging, such as orthogonal dipole anisotropy and radial tomography imaging, are widely used. However, when the fractures generated by hydraulic fracturing form a network-like pattern, orthogonal dipole anisotropy fails to accurately assess the fracturing effects. Radial tomography imaging can address this issue, but it is challenged by high manpower and time costs. This study aims to develop a more efficient and accurate method for evaluating fracturing effects in tight reservoirs using deep learning techniques. Specifically, the method utilizes dipole array acoustic logging curves recorded before and after fracturing. Manual labeling was conducted by integrating logging data interpretation results. An improved WGAN-GP was employed to generate adversarial samples for data augmentation, and fracturing effect evaluation was implemented using SE-ResNet, ResNet, and DenseNet. The experimental results demonstrated that ResNet with residual connections is more suitable for the dataset in this study, achieving higher accuracy in fracturing effect evaluation. The inclusion of the SE module further enhanced model accuracy by adaptively adjusting the weights of feature map channels, with the highest accuracy reaching 99.75%.

Feasibility of CO2 storage and enhanced gas recovery in depleted tight sandstone gas reservoirs within multi-stage fracturing horizontal wells

Injecting CO2 when the gas reservoir of tight sandstone is depleted can achieve the dual purposes of greenhouse gas storage and enhanced gas recovery (CS-EGR). To evaluate the feasibility of CO2 injection to enhance gas recovery and understand the production mechanism, a numerical simulation model of CS-EGR in multi-stage fracturing horizontal wells is established. The behavior of gas production and CO2 sequestration is then analyzed through numerical simulation, and the impact of fracture parameters on production performance is examined. Simulation results show that the production rate increases significantly and a large amount of CO2 is stored in the reservoir, proving the technical potential. However, hydraulic fractures accelerate CO2 breakthrough, resulting in lower gas recovery and lower CO2 storage than in gas reservoirs without fracturing. Increasing the length of hydraulic fractures can significantly increase CH4 production, but CO2 breakthrough will advance. Staggered and spaced perforation of hydraulic fractures in injection wells and production wells changes the fluid flow path, which can delay CO2 breakthrough and benefit production efficiency. The fracture network of massive hydraulic fracturing has a positive effect on the CS-EGR. As a result, CH4 production, gas recovery, and CO2 storage increase with the increase in stimulated reservoir volume.

Deep-learning-based natural fracture identification method through seismic multi-attribute data: a case study from the Bozi-Dabei area of the Kuqa Basin, China

Fractures play a crucial role in tight sandstone gas reservoirs with low permeability and low effective porosity. If open, they not only significantly increase the permeability of the reservoir but also serve as channels connecting the storage space. Among numerous fracture identification methods, seismic data provide unique advantages for fracture identification owing to the provision of three-dimensional information between wells. How to accurately identify the development of fractures in geological bodies between wells using seismic data is a major challenge. In this study, a tight sandstone gas reservoir in the Kuqa Basin (China) was used as an example for identifying reservoir fractures using deep-learning-based method. First, a feasibility analysis is necessary. Intersection analysis between the fracture density and seismic attributes (the characteristics of frequency, amplitude, phase, and other aspects of seismic signals) indicates that there is a correlation between the two when the fracture density exceeds a certain degree. The development of fractures is closely related to the lithology and structure, indirectly affecting differences in seismic attributes. This indicates that the use of seismic attributes for fracture identification is feasible and reasonable. Subsequently, the effective fracture density data obtained from imaging logging were used as label data, and the optimized seismic attribute near the well data were used as feature data to construct a fracture identification sample dataset. Based on a feed-forward neural network algorithm combined with natural fracture density and effectiveness control factor constraints, a trained identification model was obtained. The identification model was applied to seismic multi-attribute data for the entire work area. Finally, the accuracy of the results from the training, testing, and validation datasets were used to determine the effectiveness of the method. The relationship between the fracture identification results and the location of the fractures in the target reservoir was used to determine the reasonableness of the results. The results indicate that there is a certain relationship between multiple seismic attributes and fracture development, which can be established using deep learning models. Furthermore, the deep-learning-based seismic data fracture identification method can effectively identify fractures in the three-dimensional space of reservoirs.

Analysis of Factors Influencing Tight Sandstone Gas Production and Identification of Favorable Gas Layers in the Shan 23 Sub-Member of the Daning-Jixian Block, Eastern Ordos Basin

The Daning-Jixian Block harbors abundant tight sandstone gas resources. However, significant variations in gas production exist among the different wells within the block. A comprehensive study was conducted on key factors such as sedimentary strata and petrophysical characteristics to elucidate their impact on gas reservoir productivity. Linear regression equations were employed to classify the favorable reservoirs within the study area. The analysis revealed that within the first 6 months of production from the Shan 23 gas layer, daily gas production ranged from 2576.19 to 156,078.17 m3/d, averaging 24,037.9 m3/d. Over the first year, average daily production varied from 2185.05 to 136,806.99 m3/d, averaging 23,469.23 m3/d, indicating relatively stable production from the Shan 23 layer alone. In the dominant central area of the underwater distributary channel delta front in Shan23, the sand body exhibits a superimposed cutting type, resulting in high production rates. Conversely, the sand bodies on the periphery gradually transition to superimposed and isolated types, leading to decreased production. Through a correlation analysis of gas layer thickness, porosity, permeability, and initial gas well production, it was determined that gas production from the wells within the same layer is primarily influenced by gas layer thickness, porosity, and permeability. Gas saturation demonstrates a minimal impact on production according to single-factor analysis. The evaluated factors such as the gas productivity coefficient, energy storage coefficient, and enrichment coefficient exhibited similar distribution patterns across the study area. The high-value areas for the gas productivity coefficient, energy storage coefficient, and enrichment coefficient are concentrated in distributary channel zones and delta lobes. In contrast, regions with underdeveloped skeletal sand bodies generally display lower values for these parameters. The linear relationships between these parameters and the average gas production were calculated to further classify the favorable reservoirs in the study area. This study aimed to establish a scientific basis for the efficient development of the tight sandstone gas reservoirs within the Daning-Jixian Block.

Numerical Simulation of Non-Darcy Flow in Naturally Fractured Tight Gas Reservoirs for Enhanced Gas Recovery

In this work, we analyze non-Darcy two-component single-phase isothermal flow in naturally fractured tight gas reservoirs. The model is applied in a scenario of enhanced gas recovery (EGR) with the possibility of carbon dioxide storage. The properties of the gases are obtained via the Peng–Robinson equation of state. The finite volume method is used to solve the governing partial differential equations. This process leads to two subsystems of algebraic equations, which, after linearization and use of an operator splitting method, are solved by the conjugate gradient (CG) and biconjugate gradient stabilized (BiCGSTAB) methods for determining the pressure and fraction molar, respectively. We include inertial effects using the Barree and Conway model and gas slippage via a more recent model than Klinkenberg’s, and we use a simplified model for the effects of effective stress. We also utilize a mesh refinement technique to represent the discrete fractures. Finally, several simulations show the influence of inertial, slippage and stress effects on production in fractured tight gas reservoirs.

Efficient development technology of Upper Paleozoic Lower Shihezi tight sandstone gas reservoir in northeastern Ordos Basin

Abstract The Lower Shihezi Formation of the Upper Paleozoic at the northeastern margin of the Ordos Basin develops widely distributed thick massive and multilayer gas reservoirs. How to formulate an effective development policy is a difficult and hot spot. In this article, reservoir characteristics and production capacity influencing factors of the tight gas sandstone in Lower Shihezi Formation in this area are systematically studied, and optimization schemes of development measures for massive and multilayer gas reservoirs are proposed. The results show that the petrophysical characteristics of the small pore–mesopore type gas reservoir in the target layer are the best, with the average porosity, permeability, and coordination number of 7.6%, 0.74 mD, and 3.3, respectively. Thick sand body, high structural position, good petrophysical properties, and high drilling rate of sandstone are all conducive to drilling high production gas wells. Development policies for massive and multilayer gas reservoirs have been formulated: (1) the preferred well type for massive gas reservoir is vertical well + horizontal well, while the preferred well type for multilayer gas reservoir is horizontal well + stepped horizontal well; (2) the reasonable horizontal segment length of massive gas reservoir is 1,000 m, and the reasonable horizontal segment length of multilayer gas reservoir is 1,250 m; (3) similar to massive and multilayer gas reservoirs, the more the fracture stages, the higher the cumulative gas production, and the optimal fracture stage number of both gas reservoirs is 8; (4) the optimal fracture half-length and the angle between the fracture and the horizontal section are 140 m and 90°, respectively; and (5) the reasonable well spacing of vertical wells is 600 m and that of horizontal wells is 750 m. The development policy proposed in this study is suitable for the efficient development of complex tight sandstone gas reservoirs in similar areas.

Superposition Rate: A Developed Approach for Analyzing Production Data in Tight Gas Reservoirs

The paper examines the application of various methods to analyze production data in which the superposition principle being one of them. This principle is divided into two main methods: Superposition rate and Superposition time. Both are designed to deal with multiple flow rates within production data. The Superposition time converts the production time into constant values and uses the final flow rate value. On the other hand Superposition rate consolidates multiple rates into a single flow rate during production time. This study focuses on analysis of production data using a developed method based on superposition-rate approach. This method converts variables such as flow rate and pressure into constant values, where each flow regime has its own technique by which superposition-rate is utilized. In addition, comparison between conventional and unconventional reservoirs is involved, particularly emphasizing the benefits and drawbacks of tight gas reservoirs.Various types of superposition methods with their functions were discussed. Among them, a workflow part includes a case study of a dominated flow regime used for a hydraulic fractured single well. The workflow part of this study includes a case study of hydraulically fractured well under a dominant flow regime. Results comparing multiple rates method with super position-rate method indicate that the latter has better accuracy with (R) coefficient of (0.96). Case study on fracture reservoir also shows further validated of superposition-rate by accurately estimating drainage volume.

Accurate identification of low-resistivity gas layer in tight sandstone gas reservoirs based on optimizable neural networks

In tight sandstone reservoirs, low resistivity gas reservoirs have a certain gas production capacity, but their reservoir resistivity value is less than 20 Ω m, or their ratio to water layer resistivity is less than 2. Therefore, the identification of low resistivity gas reservoirs is a prominent problem that needs to be resolved. In this paper, on the basis of traditional experimental analysis, NMR experiments and wettability experiments were conducted. The high bound water saturation and pore fractal dimension have a significant impact on reservoir resistivity. After various experimental analysis results were preprocessed by the image pool and data pool, an optimized neural network (ONN) classification model was established based on deep learning theory. The confusion matrix result (training datasets) was 87.13%, and the ROC area of low resistivity gas layers was close to 1, indicating good recognition performance for low resistivity gas layers. The model was used to identify the properties of the J4 well reservoir section. The identification result was consistent with the actual logging resistivity values (low resistivity) and testing conclusions (gas layer). This model can accurately identify low resistivity gas reservoirs in the research area, and has important guiding significance for identifying the reservoir properties of tight sandstone gas reservoirs in the Sichuan Basin. It can provide reference value for the exploration and development of similar gas reservoirs in other regions.

Productivity Model Study of Water-Bearing Tight Gas Reservoirs Considering Micro- to Nano-Scale Effects

Tight sandstone is rich in micron- and nano-scale pores, making the two-phase flow of gas and water complex. Establishing reliable relative permeability and productivity models is an urgent issue. In this study, we first used a slip model to correct the gas phase’s no-slip Hagen–Poiseuille equation for nano- and micropores. Then, combined with the fractal theory of porous media and the tortuous capillary bundle model, we established two-phase relative permeability models for nanopores and micropores. These relative permeability models comprehensively consider the gas slippage effect, the initiation pressure gradient, the pores’ fractal characteristics, and water film mechanisms. Based on these models, we developed a three-region coupling productivity model for water-bearing tight gas reservoirs with multi-stage fractured horizontal wells. This productivity model considered the micro- and nano-scale effects and the heterogeneity of fracture networks. Then, the model was solved and validated with a field case. The results indicated that the three-region composite unsteady productivity model for water-bearing tight gas reservoirs, which incorporated micro- and nano-scale effects (with consideration of micro-scale and nano-scale phenomena in the fluid flow), could accurately predict a gas well’s productivity. An analysis of the factors influencing productivity showed that ignoring the micro- and nano-scale effects in water-bearing tight gas reservoirs will underestimate the reservoir’s productivity. The initial water saturation, the two-phase flow’s initiation pressure gradient, and capillary force are all negatively correlated with the productivity of gas wells, while the conductivity of the fractures is positively correlated with gas well productivity.

An Accurate Calculation Method on Blasingame Production Decline Model of Horizontal Well with Dumbbell-like Hydraulic Fracture in Tight Gas Reservoirs

Blasingame production decline is an effective method to obtain permeability and single-well controlled reserves. The accurate Blasingame production decline curve needs an accurate wellbore pressure approximate solution of the real-time domain. Therefore, the aim of this study is to present a simple and accurate wellbore pressure approximate solution and Blasingame production decline curves calculation method of a multi-stage fractured horizontal well (MFHW) with complex fractures. A semi-analytical model of MFHWs in circle-closed reservoirs is presented. The wellbore pressure and dimensionless pseudo-steady productivity index JDpss (1/bDpss) are verified with a numerical solution. The comparison result reaches a good match. Wellbore pressure and Blasingame production decline curves are used to analyze parameter sensitivity. Results show that when the crossflow from matrix to natural fracture appears after the pseudo-state flow regime, the value of the inter-porosity coefficient has an obvious influence on the pressure approximate solution of the pseudo-steady flow regime in naturally fractured gas reservoirs. The effects of relevant parameters on wellbore pressure and the Blasingame decline curve are also analyzed. The method of pseudo-steady productivity index JDpss can applied to all well and reservoir models.