Resource potential of gas reservoirs in South Pakistan and adjacent Indian subcontinent revealed by post-stack inversion techniques

Abstract

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Prediction of Reservoir Quality from Log-Core and Seismic Inversion Analysis with an Artificial Neural Network: A Case Study from the Sawan Gas Field, Pakistan

“…It can be observed that the porosity varies from a maximum of 25% (red color) to minimum of 5% (pink color) along with the range of AI varying from 6000 to 13,000 (g/cc) × (m/s). Herein, it is important to point out that low AI layers are consistent with high porosity values over the entire field as compared to previous studies in the region [18]. However, in the middle of the reservoir interval (vertically), high porosity layer is visible but the values of AI is also high at this particular location.…”

“…Although complex and nonlinear relationship exists between the thinly layered media and seismic waveforms, SVM established nonlinear projection relationship to capture the thinly layered media from the seismic waveform. In contrast, the inverted AI from model-based or other traditional algorithms applied previously in the same dataset have not been good enough to capture the small lithological variations [18,19]. What is more, overall lateral and vertical variations in the resolution of AI estimated from traditional inversion algorithms was poor and resulted in discontinuity of seismic event.…”

“…A linear relationship between porosity and hydrocarbon saturation versus AI can be observed with a negative slope. Ali et al [18] reported that the relationship between AI and estimated porosity is always linear in nature. Therefore, linear regression analysis was used for this study.…”

“…In the past few years, multiple studies have been carried out to evaluate the remaining potential of the C-sand interval using modern techniques [15][16][17]. Recent studies performed by Ali et al [18] and Asad and Rahim [19] indicated that model-based post-stack inversion presented good results for the spatial distribution of reservoir properties and mapping potential gas-saturated zones in the C-sand interval. Even though the model-based inversion algorithm achieved good results, the quantification of the highly productive area was scaled relative to the whole body of the reservoir.…”

A method for measuring coalbd methane content in coal strata without the loss of the gas

“…A combination of wireline geophysical logging (Szabó and Dobrók, 2013) and an empirical dependence of coalbed methane content on rank of coal and depth position (Mullen, 1989) is another indirect approach for preliminary estimation of methane resources. Recently new approaches such as seismic methods (Ali et al, 2018) began to gain popularity for indirect assessments of gas content in coal bearing strata. However all indirect methods can predict potential amount of methane in coal bearing strata as a first approximation.…”