Stochastic optimization of hydraulic fracture and horizontal well parameters in shale gas reservoirs

Rammay, Muzammil Hussain; Awotunde, Abeeb A.

doi:10.1016/j.jngse.2016.10.002

Cited by 35 publications

(9 citation statements)

References 23 publications

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“…Li et al [19] applied a dynamic simplex interpolation-based alternate subspace (DSIAS) search method for the mixed integer optimization problems associated with shale gas development projects. Rammay and Awotunde [20] simultaneously optimized the hydraulic fracture conductivity, fracture length, fracture spacing and horizontal well. Therefore, many algorithms have been proposed for the sensitivity analysis and optimal design for the enhancement of shale gas recovery.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Parameter Optimization Model for the Evaluation of Shale Gas Recovery Enhancement

et al. 2018

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Although a multi-stage hydraulically fractured horizontal well in a shale reservoir initially produces gas at a high production rate, this production rate declines rapidly within a short period and the cumulative gas production is only a small fraction (20-30%) of the estimated gas in place. In order to maximize the gas recovery rate (GRR), this study proposes a multi-parameter optimization model for a typical multi-stage hydraulically fractured shale gas horizontal well. This is achieved by combining the response surface methodology (RSM) for the optimization of objective function with a fully coupled hydro-mechanical FEC-DPM for forward computation. The objective function is constructed with seven uncertain parameters ranging from matrix to hydraulic fracture. These parameters are optimized to achieve the GRR maximization in short-term and long-term gas productions, respectively. The key influential factors among these parameters are identified. It is established that the gas recovery rate can be enhanced by 10% in the short-term production and by 60% in the long-term production if the optimized parameters are used. Therefore, combining hydraulic fracturing with an auxiliary method to enhance the gas diffusion in matrix may be an effective alternative method for the economic development of shale gas.

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Section: Introductionmentioning

confidence: 99%

A Multi-Parameter Optimization Model for the Evaluation of Shale Gas Recovery Enhancement

et al. 2018

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“…Therefore, fracturing cost mainly includes the cost of pumping fracturing fluid and proppant. A larger number of researches have been conducted on the relationship between the volume of fracturing fluid and fracture half-length (Rammay and Awotunde, 2016;Yang et al, 2017;Wang and Chen, 2019). Here, the relationship between them is obtained by referring to Rammay and Awotunde (2016) and Nordgren (1970).…”

Section: Economic Modelmentioning

confidence: 99%

“…Xu et al (2018) combined the embedded discrete fracture model (EDFM) and intelligent algorithm to maximize the NPV. Differential evolution (DE) has been employed on a shale gas reservoir simulation model to optimize fracture half-length and fracture spacing (Rammay and Awotunde, 2016). Wang and Chen (2019a) proposed a generalized differential evolution (GDE) algorithm to optimize the well spacing, fracture spacing, half-length, and conductivity of tight oil horizontal wells.…”

Section: Introductionmentioning

confidence: 99%

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

Wang

Qiao

et al. 2021

Front. Earth Sci.

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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.

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“…Gas desorption is considered using Langmuir isotherm that is replicated by solution gas ratio in a black oil model. Rammay and Awotunde (2016) integrated the differential evolution algorithm with a commercial reservoir simulator to maximize the NPV for shale gas reservoirs in terms of hydraulic fracture length, spacing, and conductivity. Langmuir gas desorption, stress dependent conductivity and local grid refinement were incorporated into a dual-porosity flow model.…”

Section: Introductionmentioning

confidence: 99%

Automatic fracture optimization for shale gas reservoirs based on gradient descent method and reservoir simulation

Chen

Wang

Yao

et al. 2021

Adv. Geo-Energy Res.

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In shale gas reservoir development, determination of hydraulic fracture geometry for horizontal wells is a demanding yet challenging task. One type of approach for hydraulic fracture optimization is based on reservoir simulation. To improve optimization efficiency and accuracy, an automatic and robust procedure integrating the gradient descent method with gas reservoir simulation has been developed. Fractured reservoir models were constructed using the "Multiple INteracting Continua" method, whereby an in-house shale gas reservoir simulator was implemented to model multiple gas transport mechanisms including non-Darcy flow, gas desorption, Klinkenberg effect, and geomechanical effect. The optimization procedure was first validated against two ideal cases and then applied to two realistic cases to optimize fracture spacing, half-length, and dimensionless fracture conductivity. It showed that the optimization results depend on optimization objective, reservoir property, natural fractures, economics and termination criteria. This gradient descent assisted fracture optimization procedure can achieve significant computational reduction and high prediction accuracy for various shale gas reservoir cases.

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Stochastic optimization of hydraulic fracture and horizontal well parameters in shale gas reservoirs

Cited by 35 publications

References 23 publications

A Multi-Parameter Optimization Model for the Evaluation of Shale Gas Recovery Enhancement

A Multi-Parameter Optimization Model for the Evaluation of Shale Gas Recovery Enhancement

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

Automatic fracture optimization for shale gas reservoirs based on gradient descent method and reservoir simulation

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