The impact of diagenesis on the reservoir quality of the early Cretaceous Lower Goru sandstones in the Lower Indus Basin, Pakistan

Abstract

The sandstone units of the Early Cretaceous Lower Goru Formation are significant reservoir for gas, oil, and condensates in the Lower Indus Basin of Pakistan. Even though these sandstones are significant reservoir rocks for hydrocarbon exploration, the diagenetic controls on the reservoir properties of the sandstones are poorly documented. For effective exploration, production, and appraisal of a promising reservoir, the diagenesis and reservoir properties must be comprehensively analyzed first. For this study, core samples from depths of more than 3100 m from the KD-01 well within the central division of the basin have been studied. These sandstones were analyzed using petrographic, X-ray diffraction, and scanning electron microscopic analyses to unravel diagenetic impacts on reservoir properties of the sandstone. Medium to coarse-grained and well-sorted sandstone have been identified during petrographic study. The sandstone are categorized as arkose and lithic arkose. Principal diagenetic events which have resulted in changing the primary characters of the sandstones are compaction, cementation, dissolution, and mineral replacement. The observed diagenetic processes can be grouped into early, burial, and late diagenesis. Chlorite is the dominant diagenetic constituent that occurs as rims, coatings, and replacing grains. The early phase of coating of authigenic chlorite has preserved the primary porosity. The recrystallization of chlorite into chamosite has massively reduced the original pore space because of its bridging structure. The current study reveals that diagenetic processes have altered the original rock properties and reservoir characteristics of the Lower Goru sandstone. These preliminary outcomes of this study have great potential to improve the understanding of diagenetic process and their impact on reservoir properties of the Lower Goru sandstone in the Lower Indus Basin and adjoining areas.

Sedimentary Facies Controls for Reservoir Quality Prediction of Lower Shihezi Member-1 of the Hangjinqi Area, Ordos Basin

“…In China, unconventional tight gas provides the most contribution of the unconventional resources and accounts for 39.2% reserves and 24.6% annual production of the total natural gas [3]. Therefore, many geoscientists are working on the reservoir quality prediction of sandstone reservoirs [4][5][6][7]. The Ordos basin (OB) is one of the most productive basins with the highest annual gas output in China [8,9].…”

Hydrocarbon Potential Assessment of Carbonate-Bearing Sediments in a Meyal Oil Field, Pakistan: Insights from Logging Data Using Machine Learning and Quanti Elan Modeling

“…Many diagenetic processes, including micritization, dolomitization, cementation, neomorphism, dissolution, compaction (mechanical and chemical, i.e., stylolitization), and fracturing, have altered the Chorgali Formation . Diagenetic processes play an important role in altering the quality of the reservoir and affect the stratigraphic–structural play significantly. ,− In the case of the Chorgali Formation within the MOF, the diagenetic processes enhancing the reservoir quality include dolomitization, dissolution, and fracturing. However, micritization, cementation, neomorphism, and compaction played major roles in reducing the reservoir properties.…”

“…Most of the recent studies are related to the discussion of the hydrocarbon potential and reservoir quality assessment. − A detailed understanding of reservoir lithofacies, rock characteristics, and hydrocarbon potential is critical for successful field development planning. − In carbonates, it is quite challenging to classify lithofacies because of lithological variations and diagenetic processes. − Recently, scientists have used advanced machine-learning techniques to identify lithofacies. − It is quite contemporary to employ robust machine-learning tools for precise prediction in oil and gas fields. − There is a stronger connection between machine learning and unsupervised learning because unsupervised learning can naturally discover patterns that are hidden without human supervision . A self-organizing map (SOM) is an advanced method for lithofacies identification .…”

“…Most of the recent studies are related to the discussion of the hydrocarbon potential and reservoir quality assessment. 23 − 26 A detailed understanding of reservoir lithofacies, rock characteristics, and hydrocarbon potential is critical for successful field development planning. 27 − 30 In carbonates, it is quite challenging to classify lithofacies because of lithological variations and diagenetic processes.…”

Application of Machine Learning for Lithofacies Prediction and Cluster Analysis Approach to Identify Rock Type

“…The Lower Goru Formation was deposited over the whole basin of the Southern Indus [33,37]. The early Cretaceous Goru Formation, which is divided into two sections (the Lower Goru Formation (LGF) and Upper Goru Formation (UGF)), superimposes the Sembar Formation [44,45]. The Goru Formation was accumulated in a shallow marine environment such as a shoreface to the fluvial-based proximal delta-front depositional framework [1,41].…”