Forecasting of Horizontal Gas Well Production Decline in Unconventional Reservoirs using Productivity, Soft Computing and Swarm Intelligence Models

Brantson, Eric Thompson; Ju, Binshan; Ziggah, Yao Yevenyo; Akwensi, Perpetual Hope; Sun, Yan; Wu, Dan; Addo, Bright

doi:10.1007/s11053-018-9415-2

Cited by 24 publications

(8 citation statements)

References 67 publications

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“…where t is the measured parameter value, p is the predicted parameter value, t is the mean measured parameter value, p is the mean predicted parameter value, and n is the total number of data points [27].…”

Section: Developing Gpr Modelsmentioning

confidence: 99%

Investigating the Predictive Performance of Gaussian Process Regression in Evaluating Reservoir Porosity and Permeability

et al. 2018

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In this paper, a new predictive model based on Gaussian process regression (GPR) that does not require iterative tuning of user-defined model parameters has been proposed to determine reservoir porosity and permeability. For this purpose, the capability of GPR was appraised statistically for predicting porosity and permeability of the southern basin of the South Yellow Sea using petrophysical well log data. Generally, the performance of GPR is deeply reliant on the type covariance function utilized. Therefore, to obtain the optimal GPR model, five different kernel functions were tested. The resulting optimal GPR model consisted of the exponential covariance function, which produced the highest correlation coefficient (R) of 0.85 and the least root mean square error (RMSE) of 0.037 and 6.47 for porosity and permeability, respectively. Comparison was further made with benchmark methods involving a back propagation neural network (BPNN), generalized regression neural network (GRNN), and radial basis function neural network (RBFNN). The statistical findings revealed that the proposed GPR is a powerful technique and can be used as a supplement to the widely used artificial neural network methods. In terms of computational speed, the GPR technique was computationally faster than the BPNN, GRNN, and RBFNN methods in estimating reservoir porosity and permeability.

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Section: Developing Gpr Modelsmentioning

confidence: 99%

Investigating the Predictive Performance of Gaussian Process Regression in Evaluating Reservoir Porosity and Permeability

et al. 2018

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“…Again, the focus of their work was on blasting effects but not in relation to presplit blasting technique. Lastly, it can be observed from the above literature reviewed that more attention needs to be geared towards this technique using AI tools [48][49][50].…”

Section: Artificial Intelligence Application In Drilling and Blastingmentioning

confidence: 99%

A Comprehensive Review of Traditional, Modern and Advanced Presplit Drilling and Blasting in the Mining and Construction Industries

Brantson,

Appiah,

Alhassan

et al. 2024

Journal of Petroleum and Mining Engineering

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Presplit drilling and blasting are frequent excavation methods used in the mining and construction industries, but they can be challenging to implement which can also lead to inconsistent results. This review identifies the key mechanisms behind presplit drilling and blasting, and discusses the significant impact that this technique has on the industry. It also emphasizes the major issues that must be addressed before presplit drilling and blasting can be properly implemented, such as the drilling program.The review then introduces the potential application of Artificial Intelligence (AI) tools in presplitting, and discusses how AI can be used to optimize the future design of presplit blast patterns, predict the performance of presplit blasts, and monitor the progress of presplit blasts in real time. The application of AI tools in presplitting has the potential to improve the safety, efficiency, and cost-effectiveness of blasting operations. The review concludes by discussing the future of drilling and blasting in the mining and construction industries, and emphasizes the role of AI optimization as a future tool in moving this field into the autonomous dimension.

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“…The results show that the proposed DNN model can integrate directly into existing hydraulic fracturing design programs. Brantson et al applied back-propagation artificial neural network (BPANN), radial basis function neural network (RBNN), and generalized regression neural network (GRNN) as proxy models to predict the historical production decreasing trend of extralow porosity tight gas reservoirs and further validated the effectiveness of the proxy models.…”

Section: Introductionmentioning

confidence: 99%

Novel Self-Adaptive Shale Gas Production Proxy Model and Its Practical Application

Qiao

Wang

et al. 2022

ACS Omega

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Recently, production optimization has gained increasing interest in the petroleum industry. The most computationally intensive and critical part of the production optimization process is the evaluation of the production function performed by the numerical reservoir simulator. Employing proxy models as a substitute for the reservoir simulator is proposed for alleviating this high computational cost. In this study, a new approach to construct adaptive proxy models for production optimization problems is proposed. An adaptive difference evolution algorithm (SaDE) optimized least-squares support vector machine (LSSVM) is used as an approximation function, while training is performed using a self-adaptive response surface experimental design (SaRSE). SaDE selects the optimal hyperparameters of LSSVM during the training process to improve the prediction accuracy of the proxy model. Cross-validation methods are used in the recursive training and network evaluation phases. The developed method is used to optimize the production of block gas reservoir models. Computational results confirm that the developed adaptive proxy model outperforms traditional regression methods. It is further verified that when the experimental data are updated, the alternative model still has high prediction accuracy when performing the objective function evaluation. The results show that the proposed proxy modeling approach enhances the entire optimization process by providing a fast approximation of the actual reservoir simulation model with better accuracy.

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Forecasting of Horizontal Gas Well Production Decline in Unconventional Reservoirs using Productivity, Soft Computing and Swarm Intelligence Models

Cited by 24 publications

References 67 publications

Investigating the Predictive Performance of Gaussian Process Regression in Evaluating Reservoir Porosity and Permeability

Investigating the Predictive Performance of Gaussian Process Regression in Evaluating Reservoir Porosity and Permeability

A Comprehensive Review of Traditional, Modern and Advanced Presplit Drilling and Blasting in the Mining and Construction Industries

Novel Self-Adaptive Shale Gas Production Proxy Model and Its Practical Application

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