A Comparative Study on the Pore Size Distribution of Different Indian Shale Gas Reservoirs for Gas Production and Potential CO₂ Sequestration

Abstract

A thorough knowledge on pore size distribution (PSD) is one of the fundamental requirements for characterization of shale gas reservoirs and for accurate estimation of their gas storage potential. However, being an important future source of energy need in India, the pore size distribution of Indian shale gas systems is not scientifically well understood. In the present study, the nanoscale pore size distributions of prospective Indian shale basins, viz., Cambay, Cauvery, Krishna-Godavari (K-G), and Damodar valley (DV), were investigated using mercury injection porosimetry (MIP) and low-pressure gas adsorption (LP-N2 and LP-CO2) techniques. The study focused on identifying the priority basins for shale gas production which can be substituted for sequestration of CO2 based on their PSDs. The samples exhibited higher thermal maturity with increasing organic content. The chemical composition of the shale samples was inferred from XRD data, which depicted higher clay content. The prominent clay minerals identified muscovite, illite, and kaolinite, which are generally flaky in nature. These minerals contributed significantly to the pore size complexity of the studied shale samples. The experimental result suggested that the samples exhibited diversified pore size characteristics. The shales are chiefly bimodal, consisting of mesopores (2–50 nm) and micropores (<2 nm). The micropores were efficiently accessed using CO2, while N2 was effective on characterizing the mesopore region. MIP analysis was used to infer the pore throat area. The average pore diameter of samples ranged from 3.38 nm in Damodar valley to 3.94 nm in thr K-G basin, while Cauvery and Cambay basin samples possessed 3.88 and 3.86 nm, respectively. Both N2 and CO2 adsorption (type II and type I, respectively) suggested the presence of micropore infilling in all of the basin samples. However, Cauvery basin samples were inferred with higher mesopore content as compared to other basins and possessed higher quartz percentage, making it more appropriate for hydraulic fracturing. The Cauvery basin showed enrichment of larger pore sizes. The results signified that the Damodar valley shales have a higher affinity toward CO2 adsorption. This suggested that the basin could be a better host for future carbon storage, compared to other basin pore structures.

Comparative Study on the Adsorption Capacities of the Three Black Phosphorus-Based Materials for Methylene Blue in Water

“…It meant BPRS had the most active adsorption sites, followed by BPR, and finally BP. The pore sizes of micropore, macropore, and mesopore were defined as less than 2 nm, greater than 50 nm, and between 2 and 50 nm, respectively [49]. As mesopore was conducive to the distribution of active sites of the adsorbent, it increased the contact between active sites and the adsorbate, which was beneficial for adsorption [50].…”

Will the future of shale reservoirs lie in CO2 geological sequestration?

“…An increase in permeability and CO 2 adsorption with either increasing TOC or illite content was concluded. Some studies indicated that TOC increases with higher thermal maturity [22]. TOC (thermal maturity) and clay minerals (illite, muscovite, and kaolinite) content representing the organic and inorganic adsorbing components of gas shales can be another set of parameters for prescreening criteria.…”

A case study of petrophysical rock typing and permeability prediction using machine learning in a heterogenous carbonate reservoir in Iran

“…These methods are generally used to estimate the number of parameters that are otherwise technically or theoretically challenging to obtain 29 , 30 . The data-driven approaches have been used in various disciplines of the industry, such as approximation of multiple parameters related to carbon capture and sequestration 31 , 32 , predicting oil recovery 33 . And estimation of rock and fluid properties 32 , 34 – 36 .…”

“…The data-driven approaches have been used in various disciplines of the industry, such as approximation of multiple parameters related to carbon capture and sequestration 31 , 32 , predicting oil recovery 33 . And estimation of rock and fluid properties 32 , 34 – 36 .…”