Rock physics modeling to evaluate clastic reservoirs: a case study from the Lower Goru Formation, Middle Indus Basin, Pakistan

Hussain, Wakeel; Luo, Miao; Ali, Muhammad; Hussain, Hadi; Ali, Sajid; Iqbal, Jaffar; Nyakilla, Edwin E; Asghar, Ali

doi:10.1007/s12665-024-11676-1

Environ Earth Sci

2024

DOI: 10.1007/s12665-024-11676-1

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Rock physics modeling to evaluate clastic reservoirs: a case study from the Lower Goru Formation, Middle Indus Basin, Pakistan

Wakeel Hussain,

Miao Luo,

Muhammad Ali

et al.

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2024

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Cited by 2 publications

References 52 publications

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Optimizing reservoir characterization: insights from integrated data analysis

Ibe,

Oturu,

Anyanwu

2024

Discov Geosci

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Optimizing reservoir characterization: insights from integrated data analysis

Ibe,

Oturu,

Anyanwu

2024

Discov Geosci

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Advancing Reservoir Evaluation: Machine Learning Approaches for Predicting Porosity Curves

Ali,

Fu,

Chen

et al. 2024

Energies

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Porosity assessment is a vital component for reservoir evaluation in the oil and gas sector, and with technological advancement, reliance on conventional methods has decreased. In this regard, this research aims to reduce reliance on well logging, purposing successive machine learning (ML) techniques for precise porosity measurement. So, this research examines the prediction of the porosity curves in the Sui main and Sui upper limestone reservoir, utilizing ML approaches such as an artificial neural networks (ANN) and fuzzy logic (FL). Thus, the input dataset of this research includes gamma ray (GR), neutron porosity (NPHI), density (RHOB), and sonic (DT) logs amongst five drilled wells located in the Qadirpur gas field. The ANN model was trained using the backpropagation algorithm. For the FL model, ten bins were utilized, and Gaussian-shaped membership functions were chosen for ideal correspondence with the geophysical log dataset. The closeness of fit (C-fit) values for the ANN ranged from 91% to 98%, while the FL model exhibited variability from 90% to 95% throughout the wells. In addition, a similar dataset was used to evaluate multiple linear regression (MLR) for comparative analysis. The ANN and FL models achieved robust performance as compared to MLR, with R2 values of 0.955 (FL) and 0.988 (ANN) compared to 0.94 (MLR). The outcomes indicate that FL and ANN exceed MLR in predicting the porosity curve. Moreover, the significant R2 values and lowest root mean square error (RMSE) values support the potency of these advanced approaches. This research emphasizes the authenticity of FL and ANN in predicting the porosity curve. Thus, these techniques not only enhance natural resource exploitation within the region but also hold broader potential for worldwide applications in reservoir assessment.

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Rock physics modeling to evaluate clastic reservoirs: a case study from the Lower Goru Formation, Middle Indus Basin, Pakistan

Cited by 2 publications

References 52 publications

Optimizing reservoir characterization: insights from integrated data analysis

Optimizing reservoir characterization: insights from integrated data analysis

Advancing Reservoir Evaluation: Machine Learning Approaches for Predicting Porosity Curves

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