Study of flow mechanisms of shale gas and establishment of gas production model

2015 ◽  
Author(s):  
S. Mishra ◽  
S. Soni
Keyword(s):  
Shale Gas ◽  
Gas Production ◽  
Production Model ◽  
Flow Mechanisms

Diffusion and Flow Mechanisms of Shale Gas through Matrix Pores and Gas Production Forecasting

2013 ◽  
Author(s):  
Juntai Shi ◽  
Lei Zhang ◽  
Yuansheng Li ◽  
Wei Yu ◽  
Xiangnan He ◽  
...  
Keyword(s):  
Shale Gas ◽  
Gas Production ◽  
Production Forecasting ◽  
Flow Mechanisms

scholarly journals Numerical Simulation Study on Seepage Theory of a Multi-Section Fractured Horizontal Well in Shale Gas Reservoirs Based on Multi-Scale Flow Mechanisms

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2329 ◽  
Author(s):  
Chao Tang ◽  
Xiaofan Chen ◽  
Zhimin Du ◽  
Ping Yue ◽  
Jiabao Wei
Keyword(s):  
Shale Gas ◽  
Large Scale ◽  
Gas Diffusion ◽  
Gas Production ◽  
Hydraulic Fractures ◽  
Klinkenberg Effect ◽  
Stimulated Reservoir Volume ◽  
Multi Scale ◽  
Reservoir Volume ◽  
Flow Mechanisms

Aimed at the multi-scale fractures for stimulated reservoir volume (SRV)-fractured horizontal wells in shale gas reservoirs, a mathematical model of unsteady seepage is established, which considers the characteristics of a dual media of matrix and natural fractures as well as flow in the large-scale hydraulic fractures, based on a discrete-fracture model. Multi-scale flow mechanisms, such as gas desorption, the Klinkenberg effect, and gas diffusion are taken into consideration. A three-dimensional numerical model based on the finite volume method is established, which includes the construction of spatial discretization, calculation of average pressure gradient, and variable at interface, etc. Some related processing techniques, such as boundedness processing upstream and downstream of grid flow, was used to limit non-physical oscillation at large-scale hydraulic fracture interfaces. The sequential solution is performed to solve the pressure equations of matrix, natural, and large-scale hydraulic fractures. The production dynamics and pressure distribution of a multi-section fractured horizontal well in a shale gas reservoir are calculated. Results indicate that, with the increase of the Langmuir volume, the average formation pressure decreases at a slow rate. Simultaneously, the initial gas production and the contribution ratio of the desorbed gas increase. With the decrease of the pore size of the matrix, gas diffusion and the Klinkenberg effect have a greater impact on shale gas production. By changing the fracture half-length and the number of fractured sections, we observe that the production process can not only pursue the long fractures or increase the number of fractured sections, but also should optimize the parameters such as the perforation position, cluster spacing, and fracturing sequence. The stimulated reservoir volume can effectively control the shale reservoir.

Quad-porosity shale systems – a review

2016 ◽  
Vol 13 (6) ◽  
pp. 529-539 ◽  
Author(s):  
Samarth D. Patwardhan ◽  
Fatemeh Famoori ◽  
Suresh Kumar Govindarajan
Keyword(s):  
Shale Gas ◽  
Experimental Studies ◽  
Gas Diffusion ◽  
Gas Production ◽  
Total Production ◽  
Actual Process ◽  
Coupled Flow ◽  
Production Forecast ◽  
Content Type ◽  
Flow Mechanisms

Purpose This paper aims to review the quad-porosity shale system from a production standpoint. Understanding the complex but coupled flow mechanisms in such reservoirs is essential to design appropriate completions and further, optimally produce them. Dual-porosity and dual permeability models are most commonly used to describe a typical shale gas reservoir. Design/methodology/approach Characterization of such reservoirs with extremely low permeability does not aptly capture the physics and complexities of gas storage and flow through their existing nanopores. This paper reviews the methods and experimental studies used to describe the flow mechanisms of gas through such systems, and critically recommends the direction in which this work could be extended. A quad-porosity shale system is defined not just as porosity in the matrix and fracture, but as a combination of multiple porosity values. Findings It has been observed from studies conducted that shale gas production modeled with conventional simulator/model is seen to be much lower than actually observed in field data. This paper reviews the various flow mechanisms in shale nanopores by capturing the physics behind the actual process. The contribution of Knudson diffusion and gas slippage, gas desorption and gas diffusion from Kerogen to total production is studied in detail. Originality/value The results observed from experimental studies and simulation runs indicate that the above effects should be considered while modeling and making production forecast for such reservoirs.

scholarly journals A Novel Shale Gas Production Prediction Model Based on Machine Learning and Its Application in Optimization of Multistage Fractured Horizontal Wells

2021 ◽  
Vol 9 ◽  
Author(s):  
Huijun Wang ◽  
Lu Qiao ◽  
Shuangfang Lu ◽  
Fangwen Chen ◽  
Zhixiong Fang ◽  
...  
Keyword(s):  
Numerical Simulation ◽  
Simulation Model ◽  
Shale Gas ◽  
Horizontal Well ◽  
Gas Production ◽  
Production Model ◽  
Support Vector ◽  
Gas Production Prediction ◽  
Half Length ◽  
Production Prediction

Shale gas production prediction and horizontal well parameter optimization are significant for shale gas development. However, conventional reservoir numerical simulation requires extensive resources in terms of labor, time, and computations, and so the optimization problem still remains a challenge. Therefore, we propose, for the first time, a new gas production prediction methodology based on Gaussian Process Regression (GPR) and Convolution Neural Network (CNN) to complement the numerical simulation model and achieve rapid optimization. Specifically, through sensitivity analysis, porosity, permeability, fracture half-length, and horizontal well length were selected as influencing factors. Second, the n-factorial experimental design was applied to design the initial experiment and the dataset was constructed by combining the simulation results with the case parameters. Subsequently, the gas production model was built by GPR, CNN, and SVM based on the dataset. Finally, the optimal model was combined with the optimization algorithm to maximize the Net Present Value (NPV) and obtain the optimal fracture half-length and horizontal well length. Experimental results demonstrated the GPR model had prominent modeling capabilities compared with CNN and Support Vector Machine (SVM) and achieved the satisfactory prediction performance. The fracture half-length and well length optimized by the GPR model and reservoir numerical simulation model converged to almost the same values. Compared with the field reference case, the optimized NPV increased by US$ 7.43 million. Additionally, the time required to optimize the GPR model was 1/720 of that of numerical simulation. This work enriches the knowledge of shale gas development technology and lays the foundation for realizing the scale-benefit development for shale gas, so as to realize the integration of geological engineering.

Understanding Shale Gas Flow Behavior Using Numerical Simulation

SPE Journal ◽  
2015 ◽  
Vol 20 (01) ◽  
pp. 142-154 ◽  
Author(s):  
Hao Sun ◽  
Adwait Chawathé ◽  
Hussein Hoteit ◽  
Xundan Shi ◽  
Lin Li
Keyword(s):  
Mass Transfer ◽  
Organic Matter ◽  
Shale Gas ◽  
Inorganic Material ◽  
Gas Transport ◽  
Knudsen Diffusion ◽  
Gas Production ◽  
Gas Reservoir ◽  
Shale Gas Reservoir ◽  
Flow Mechanisms

Summary Shale gas has changed the energy equation around the world, and its impact has been especially profound in the United States. It is now generally agreed that the fabric of shale systems comprises primarily organic matter, inorganic material, and natural fractures. However, the underlying flow mechanisms through these multiporosity and multipermeability systems are poorly understood. For instance, debate still exists about the predominant transport mechanism (diffusion, convection, and desorption), as well as the flow interactions between organic matter, inorganic matter, and fractures. Furthermore, balancing the computational burden of precisely modeling the gas transport through the pores vs. running full reservoir scale simulation is also contested. To that end, commercial reservoir simulators are developing new shale gas options, but some, for expediency, rely on simplification of existing data structures and/or flow mechanisms. We present here the development of a comprehensive multimechanistic (desorption, diffusion, and convection), multiporosity (organic materials, inorganic materials, and fractures), and multipermeability model that uses experimentally determined shale organic and inorganic material properties to predict shale gas reservoir performance. Our multimechanistic model takes into account gas transport caused by both pressure driven convection and concentration driven diffusion. The model accounts for all the important processes occurring in shale systems, including desorption of multicomponent gas from the organics' surface, multimechanistic organic/inorganic material mass transfer, multimechanistic inorganic material/fracture network mass transfer, and production from a hydraulically fractured wellbore. Our results show that a dual porosity, dual permeability (DPDP) model with Knudsen diffusion is generally adequate to model shale gas reservoir production. Adsorption can make significant contributions to original gas in place, but is not important to gas production because of adsorption equilibrium. By comparing triple porosity, dual permeability; DPDP; and single porosity, single permeability formulations under similar conditions, we show that Knudsen diffusion is a key mechanism and should not be ignored under low matrix pressure (Pematrix) cases, whereas molecular diffusion is negligible in shale dry gas production. We also guide the design of fractures by analyzing flow rate limiting steps. This work provides a basis for long term shale gas production analysis and also helps define value adding laboratory measurements.

scholarly journals New Hybrid Approach for Developing Automated Machine Learning Workflows: A Real Case Application in Evaluation of Marcellus Shale Gas Production

Fuels ◽  
2021 ◽  
Vol 2 (3) ◽  
pp. 286-303
Author(s):  
Vuong Van Pham ◽  
Ebrahim Fathi ◽  
Fatemeh Belyadi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Shale Gas ◽  
Field Data ◽  
Marcellus Shale ◽  
Hybrid Approach ◽  
Gas Production ◽  
Bayesian Optimization ◽  
Real Field ◽  
Engineering Problems

The success of machine learning (ML) techniques implemented in different industries heavily rely on operator expertise and domain knowledge, which is used in manually choosing an algorithm and setting up the specific algorithm parameters for a problem. Due to the manual nature of model selection and parameter tuning, it is impossible to quantify or evaluate the quality of this manual process, which in turn limits the ability to perform comparison studies between different algorithms. In this study, we propose a new hybrid approach for developing machine learning workflows to help automated algorithm selection and hyperparameter optimization. The proposed approach provides a robust, reproducible, and unbiased workflow that can be quantified and validated using different scoring metrics. We have used the most common workflows implemented in the application of artificial intelligence (AI) and ML in engineering problems including grid/random search, Bayesian search and optimization, genetic programming, and compared that with our new hybrid approach that includes the integration of Tree-based Pipeline Optimization Tool (TPOT) and Bayesian optimization. The performance of each workflow is quantified using different scoring metrics such as Pearson correlation (i.e., R2 correlation) and Mean Square Error (i.e., MSE). For this purpose, actual field data obtained from 1567 gas wells in Marcellus Shale, with 121 features from reservoir, drilling, completion, stimulation, and operation is tested using different proposed workflows. A proposed new hybrid workflow is then used to evaluate the type well used for evaluation of Marcellus shale gas production. In conclusion, our automated hybrid approach showed significant improvement in comparison to other proposed workflows using both scoring matrices. The new hybrid approach provides a practical tool that supports the automated model and hyperparameter selection, which is tested using real field data that can be implemented in solving different engineering problems using artificial intelligence and machine learning. The new hybrid model is tested in a real field and compared with conventional type wells developed by field engineers. It is found that the type well of the field is very close to P50 predictions of the field, which shows great success in the completion design of the field performed by field engineers. It also shows that the field average production could have been improved by 8% if shorter cluster spacing and higher proppant loading per cluster were used during the frac jobs.

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