Hydrocarbon source rock evaluation and quantification of organic richness from correlation of well logs and geochemical data: A case study from the sembar formation, Southern Indus Basin, Pakistan

Aziz, Haroon; Ehsan, Muhsan; Ali, Abid; Khan, Humayun Khalil; Khan, Abdullah

doi:10.1016/j.jngse.2020.103433

Cited by 39 publications

(20 citation statements)

References 33 publications

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“…There are three methods to determine geochemical parameters of organic-rich shale such as direct measurement, a single well log method, 38 and a composite well log method. 39 TOC can be measured directly in the laboratory in different ways such as filter acidification, 40 nonfilter acidification, 40 total minus coulometric, 41 Rock-Eval, 42 laser-induced pyrolysis, 43 and diffuse reflectance infrared Fourier transformation spectroscopy (DRIFTS). 34 Indirect methods involve the utilization of petrophysical well logs and seismic data.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Approaches to Predict Thermal Maturity Indices of Organic Matter Using Artificial Neural Networks

et al. 2020

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Prediction of thermal maturity index parameters in organic shales plays a critical role in defining the hydrocarbon prospect and proper economic evaluation of the field. Hydrocarbon potential in shales is evaluated using the percentage of organic indices such as total organic carbon (TOC), thermal maturity temperature, source potentials, and hydrogen and oxygen indices. Direct measurement of these parameters in the laboratory is the most accurate way to obtain a representative value, but, at the same time, it is very expensive. In the absence of such facilities, other approaches such as analytical solutions and empirical correlations are used to estimate the organic indices in shale. The objective of this study is to develop data-driven machine learning-based models to predict continuous profiles of geochemical logs of organic shale formation. The machine learning models are trained using the petrophysical wireline logs as input and the corresponding laboratory-measured core data as a target for Barnett shale formations. More than 400 log data and the corresponding core data were collected for this purpose. The petrophysical wireline logs are γ-ray, bulk density, neutron porosity, sonic transient time, spontaneous potential, and shallow resistivity logs. The corresponding core data includes the experimental results from the Rock-Eval pyrolysis and Leco TOC measurements. A backpropagation artificial neural network coupled with a particle swarm optimization algorithm was used in this work. In addition to the development of optimized PSO-ANN models, explicit empirical correlations are also extracted from the fine-tuned weights and biases of the optimized models. The proposed models work with a higher accuracy within the range of the data set on which the models are trained. The proposed models can give real-time quantification of the organic matter maturity that can be linked with the real-time drilling operations and help identify the hotspots of mature organic matter in the drilled section.

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Section: Introductionmentioning

confidence: 99%

Data-Driven Approaches to Predict Thermal Maturity Indices of Organic Matter Using Artificial Neural Networks

et al. 2020

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“…For example, Kamali and Mirshady (2004) quantified the correlation between wireline logs (sonic, density, neutron, and resistivity logs) and total organic carbon using the "ΔLogR" calculation approach. Also, Aziz et al. (2020) estimated TOC using different logs relationship, including the method of "ΔLogR" and found that it is a high accuracy method for identifying the TOC from the wireline logs.…”

Section: Discussionmentioning

confidence: 99%

“…This technique has been calibrated and confirmed over the last decades by many such as Zheng et al. (2021) , Aziz et al. (2020) , Shalaby et al.…”

Section: Introductionmentioning

confidence: 87%

Evaluation of the hydrocarbon source rock and the reservoir characterization of the Minagish Formation using wireline logs

Aladwani

2022

Heliyon

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“…The Precambrian basement and majority of the Cretaceous rocks within the LIB of the study area are tectonically stable [37]. The Precambrian basement is trigger by many lows and highs, which are the cause of significant lateral and vertical thickness variants of overlying sedimentary cover [38,39]. JKH demonstrated as horst is situated in the SGF amongst these highs and lows [40].…”

Section: Stratigraphic and Structural Characteristics Of The Study Areamentioning

confidence: 99%

Controls on Reservoir Heterogeneity of a Shallow-Marine Reservoir in Sawan Gas Field, SE Pakistan: Implications for Reservoir Quality Prediction Using Acoustic Impedance Inversion

Ashraf

Zhang

Anees

et al. 2020

Water

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The precise characterization of reservoir parameters is vital for future development and prospect evaluation of oil and gas fields. C-sand and B-sand intervals of the Lower Goru Formation (LGF) within the Lower Indus Basin (LIB) are proven reservoirs. Conventional seismic amplitude interpretation fails to delineate the heterogeneity of the sand-shale facies distribution due to limited seismic resolution in the Sawan gas field (SGF). The high heterogeneity and low resolution make it challenging to characterize the reservoir thickness, reservoir porosity, and the factors controlling the heterogeneity. Constrained sparse spike inversion (CSSI) is employed using 3D seismic and well log data to characterize and discriminate the lithofacies, impedance, porosity, and thickness (sand-ratio) of the C- and B-sand intervals of the LGF. The achieved results disclose that the CSSI delineated the extent of lithofacies, heterogeneity, and precise characterization of reservoir parameters within the zone of interest (ZOI). The sand facies of C- and B-sand intervals are characterized by low acoustic impedance (AI) values (8 × 106 kg/m2s to 1 × 107 kg/m2s), maximum sand-ratio (0.6 to 0.9), and maximum porosity (10% to 24%). The primary reservoir (C-sand) has an excellent ability to produce the maximum yield of gas due to low AI (8 × 106 kg/m2s), maximum reservoir thickness (0.9), and porosity (24%). However, the secondary reservoir (B-sand) also has a good capacity for gas production due to low AI (1 × 107 kg/m2s), decent sand-ratio (0.6), and average porosity (14%), if properly evaluated. The time-slices of porosity and sand-ratio maps have revealed the location of low-impedance, maximum porosity, and maximum sand-ratio that can be exploited for future drillings. Rock physics analysis using AI through inverse and direct relationships successfully discriminated against the heterogeneity between the sand facies and shale facies. In the corollary, we proposed that pre-conditioning through comprehensive petrophysical, inversion, and rock physics analysis are imperative tools to calibrate the factors controlling the reservoir heterogeneity and for better reservoir quality measurement in the fluvial shallow-marine deltaic basins.

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Hydrocarbon source rock evaluation and quantification of organic richness from correlation of well logs and geochemical data: A case study from the sembar formation, Southern Indus Basin, Pakistan

Cited by 39 publications

References 33 publications

Data-Driven Approaches to Predict Thermal Maturity Indices of Organic Matter Using Artificial Neural Networks

Data-Driven Approaches to Predict Thermal Maturity Indices of Organic Matter Using Artificial Neural Networks

Evaluation of the hydrocarbon source rock and the reservoir characterization of the Minagish Formation using wireline logs

Controls on Reservoir Heterogeneity of a Shallow-Marine Reservoir in Sawan Gas Field, SE Pakistan: Implications for Reservoir Quality Prediction Using Acoustic Impedance Inversion

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