Comparative and Statistical Analysis of Core-Calibrated Porosity with Log-Derived Porosity for Reservoir Parameters Estimation of the Zamzama GAS Field, Southern Indus Basin, Pakistan

Munir, Muhammad Nofal; Zafar, Mohammad; Ehsan, Muhsan

doi:10.1007/s13369-022-07523-9

Cited by 8 publications

(5 citation statements)

References 22 publications

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“…The low Vsh values (less than 30%) along with high Vsh values (10−15%) indicate prominent reservoirs of the LIB. 92,93 Overall, low Vsh and high PHIE with low Sw are observed in the study area, which clarifies the distribution of the Binterval channelized sand body in the study area, which is more pronouncedly delineated by the facies distribution mapping. Good portions of potential capabilities are observed in the field that can be tested for additional wells and enhanced production from the field.…”

Section: Discussionsupporting

confidence: 64%

ResNet and CWT Fusion: A New Paradigm for Optimized Heterogeneous Thin Reservoir Evaluation

Akram,

Akhter,

et al. 2024

ACS Omega

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The endeavor to explore and characterize oil and gas reservoirs presents significant challenges due to the inherent heterogeneities that are further compounded by the existence of thin sand layers encapsulated in shale strata. This complexity is intensified by limited and low-resolution seismic data, missing critical well-log information, and inaccessible angle stack data. Conventional reservoir classification approaches have struggled to address these issues, primarily due to their limitations in handling missing data effectively and, hence, precise estimations. This study focuses on the characterization of thin, heterogeneous potential sands of the B-interval within the Lower Goru Formation, a proven gas reservoir in the Badin area. The reservoir sands with varying thicknesses are assessed in detail for their optimized description and field productions by handling challenges, including low seismic resolutions, heterogeneities, and missing data sets. An innovative solution is developed based on the integration of continuous wavelet transform (CWT) and machine learning (ML) techniques for the approximation of missing data sets, i.e., S-wave (DTS), along with enhanced elastic and petrophysical properties. The improved properties are augmented by the high resolution attained by CWT and captured variability more profoundly through the implication of residual neural networks (ResNet). The limitations of conventional approaches are harnessed by ML solutions that operate with limited input data and deliver significantly improved results in characterizing enigmatic thin sand reservoirs. The high-frequency petroelastic properties reliably determined the thin heterogeneous potential sand bodies and illuminated a channelized play fairway that can be tested for additional wells with low-risk involvement.

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Section: Discussionsupporting

confidence: 64%

ResNet and CWT Fusion: A New Paradigm for Optimized Heterogeneous Thin Reservoir Evaluation

Akram,

Akhter,

et al. 2024

ACS Omega

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“…Figures – illustrate the results of petrophysical cluster analysis, where portions labeled with electrofacies in the last track demonstrate to which rock group the Pab Formation belongs to. The parameters used for cluster analysis include raw log for curves sonic and bulk density, whereas calculated core calibrated porosity, permeability, and water saturation represent the physical properties of pore fluids, shale, and mineral components as well as the textural properties of rocks. , Core-calibrated porosity provides more accurate results for different reservoir parameters compared to porosities derived from conventional petrophysics . On the K-mean values of petrophysical properties tabulated in Table , the cluster analysis technique showed the quality of four rock types.…”

Section: Resultsmentioning

confidence: 99%

“…18,37 Core-calibrated porosity provides more accurate results for different reservoir parameters compared to porosities derived from conventional petrophysics. 15 On the Kmean values of petrophysical properties tabulated in Table 2, the cluster analysis technique showed the quality of four rock types. According to K-mean values for every cluster, four rock types are devised to show the quality of the reservoir and are given in Table 3.…”

Section: Reservoir Characterization and Rockmentioning

confidence: 97%

“…12 Previously, the rock types in the study area were defined with the help of cluster analysis using different input curves (Table 1, 14). According to Munir et al, 15 core-calibrated porosity provides more accurate results for different reservoir parameters compared to porosities derived from conventional petrophysics. 15 To get better prediction accuracy and reach better prediction performance for the studied heterogeneous reservoir, we have used different curves, and core calibrated effective porosity and water saturation are used, along with other curves.…”

Section: Introductionmentioning

confidence: 99%

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A Comparative Study Based on Petrophysical and Cluster Analysis Approach for Identification of Rock Types in Heterogeneous Sandstone Reservoirs

Munir,

Zafar,

Ali

et al. 2024

ACS Omega

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To delineate a powerful reservoir model, rock type identification is an essential task. Recognizing intervals with promising reservoir quality in a heterogeneous reservoir, such as the Pab Formation, using well logs is critical for better exploration, because coring programs are always impractical due to time and cost constraints. Rock types are described by specific log responses, which are ultimately distinguished with the help of electrofacies. The current study uses a cluster analysis technique for the evaluation of reservoir rock types in the identified sand units. Kmeans cluster analysis is employed to define electrofacies, which are ultimately classified into four rock types on the basis of reservoir quality, from bad to excellent. Rock typing using cluster analysis has been done for four wells, and a correlation has been made to depict changes in electrofacies. From well-to-well correlation, it can be inferred that the reservoir quality of the Pab Formation at the lower portion of Zamzama-02 and 05 wells is excellent and is defined by rock type 4. The Zamzama-03 well in the southwestern region, on the other hand, has good to moderate reservoir quality, as demonstrated by dominating rock types 3 and 2, respectively. The applied prediction technique to the studied field provides continuous rock type identification for the entire reservoir. Using this methodology in defining rock type is cost-effective, requires less time in the demarcation of zones of interest, and is more accurate than manual analysis of the heterogeneous and thick Pab Formation. The studied approach is not only useful in the exploitation of the heterogeneous Pab Formation but also can be applied to other heterogeneous sandstone reservoirs elsewhere.

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“…The seismic imaging method provides an invaluable tool for petroleum exploration and optimizing the efficient production of developed petroleum fields [ 17 ]. To evaluate reservoir potential for the integrated study well logs, interval velocities, seismic data, petrophysical modeling, and seismic characteristics are valuable tools [ 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

The assessment of reservoir potential of Permian to Eocene reservoirs of Minwal-Joyamair fields, upper Indus basin, Pakistan

Lateef

Ehsan

Ali

et al. 2023

Heliyon

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Comparative and Statistical Analysis of Core-Calibrated Porosity with Log-Derived Porosity for Reservoir Parameters Estimation of the Zamzama GAS Field, Southern Indus Basin, Pakistan

Cited by 8 publications

References 22 publications

ResNet and CWT Fusion: A New Paradigm for Optimized Heterogeneous Thin Reservoir Evaluation

ResNet and CWT Fusion: A New Paradigm for Optimized Heterogeneous Thin Reservoir Evaluation

A Comparative Study Based on Petrophysical and Cluster Analysis Approach for Identification of Rock Types in Heterogeneous Sandstone Reservoirs

The assessment of reservoir potential of Permian to Eocene reservoirs of Minwal-Joyamair fields, upper Indus basin, Pakistan

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