Reservoir quality and facies modeling of the early Eocene carbonate stratigraphic unit of the Middle Indus Basin, Pakistan

Khalid, Perveiz; Ehsan, Muhammd Irfan; Khurram, Shahzada; Ullah, Irfan; Ahmad, Qazi Adnan

doi:10.3389/feart.2022.1063877

Cited by 3 publications

(2 citation statements)

References 52 publications

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“…The petrophysical characterization of these carbonate rocks was extremely difficult due to heterogeneity and it exhibited a complex pore network. Based on the data obtained from well logs, these limestone reservoirs exhibited great variations in porosities ranging from 2-36%, with an average porosity of 22% [41]. In some places, these carbonates indicated microfractures and vugs, which resulted in high porosity values.…”

Section: Methodsmentioning

confidence: 99%

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Wettability Alteration of Limestone Carbonate Cores Using Methylene Blue and Alumina-Based Nanofluid: Implications for EOR

et al. 2023

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Wettability is a key parameter for optimizing the residual oil recovery from geological rock formations and it provides a path for improved oil recovery and geo-storage of energy. Thus, the key motive behind wettability alteration from hydrophobic to hydrophilic is to enhance the oil productivity. Thus, this work concentrates on Sui main limestone reservoir core samples’ wettability alteration (altering their surface wetting behavior from an oil-wet to water-wet state) for enhanced oil recovery. Hence, we examine the effectiveness of alumina nanofluid as well as a new chemical methyl blue to alter the wettability. Methyl blue is released on a large scale from various industries, i.e., pharma, textile, and food industries, which is a key environmental concern; subsequently, it contaminates the water table. Hence, the study explores the effects of MB and alumina nanofluid on wettability. The effect of nanofluids formulated via dispersing the alumina nanoparticles in aqueous solutions at various concentrations (0. 0.05, 0.3, 0.50, 0.75, and 1.0 wt. %) were tested for wettability modifications under different physio-thermal conditions. Subsequently, the wettability change was examined for these samples treated with different concentrations of MB (10, 15, 30, 50, and 100 mg/L) for 7 days at two different temperatures (25 and 50 °C). The results show that the hydrophobicity of the SML carbonate rock significantly reverses while treating with alumina nanofluids and MB. Thus, the wettability modification/reversal via the treatment of MB and alumina nanofluids can be an effective mechanism for hydrogen injections and EOR processes.

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Section: Methodsmentioning

confidence: 99%

“…In some places, these carbonates indicated microfractures and vugs, which resulted in high porosity values. These microcracks were interconnected well, thus allowing the fluid to be produced more effectively [40,41].…”

Section: Methodsmentioning

confidence: 99%

Wettability Alteration of Limestone Carbonate Cores Using Methylene Blue and Alumina-Based Nanofluid: Implications for EOR

et al. 2023

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Assessing the hydrocarbon potential of the Kadanwari gas field using integrated seismic and petrophysical data

Khan,

Ahmed,

Naseer

et al. 2024

J Petrol Explor Prod Technol

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Kadanwari is a major gas-producing field in Pakistan's Lower Indus Basin (LIB), extensively explored for optimized production. However, the reservoir sands of the Lower Goru Formation (LGF), deposited in a complex river-dominated delta, bear severe variability and hinder accurate facies approximation for optimal production. Furthermore, the regionally extended NNW-SSE directed horst and graben structures significantly compartmentalized these reservoir facies. The main E-sand interval is analyzed for its geological properties, depositional environment, and distribution. The integration of various approaches, including seismic interpretation, attribute extraction, well-based facies modeling, and petrophysical evaluation, proved significant in evaluating the heterogeneous and tectonically influenced E-sands. The discontinuity attribute substantially highlighted the structural style and aided in analyzing the geometries of faults. The low values of the frequency attribute (< 10 Hz) signified the entrapped gas-bearing sands along the faulted zones. The high responses of instantaneous amplitude and sweetness profoundly illuminated the gas-significant deposits throughout the field in association with the well-identified gas-prone sand facies. The outcomes of the neutron-density crossplot depicted gas-bearing sands having low density (< 2.3 g/cc) and good porosity (12%) with the assessment of various cements. The facies modeling distinguished between clean and intermixed sand-shale reservoir zones. Petrophysical analysis revealed a net pay of 14 m within E-sand having gas saturation of about 68%. The adopted approach is robust and efficient, employing a limited data set for developing well-associated seismic responses for potential zone delineation within structural arrangements. The techniques can be optimistic about the LGF's complex potential sands demarcation throughout the Indus Basin.

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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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Reservoir quality and facies modeling of the early Eocene carbonate stratigraphic unit of the Middle Indus Basin, Pakistan

Cited by 3 publications

References 52 publications

Wettability Alteration of Limestone Carbonate Cores Using Methylene Blue and Alumina-Based Nanofluid: Implications for EOR

Wettability Alteration of Limestone Carbonate Cores Using Methylene Blue and Alumina-Based Nanofluid: Implications for EOR

Assessing the hydrocarbon potential of the Kadanwari gas field using integrated seismic and petrophysical data

Advancing Reservoir Evaluation: Machine Learning Approaches for Predicting Porosity Curves

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