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.

The identification of small scale faults (SSFs) and fractures provides an improved understanding of geologic structural features and can be exploited for future drilling prospects. Conventional SSF and fracture characterization are challenging and time-consuming. Thus, the current study was conducted with the following aims: (a) to provide an effective way of utilizing the seismic data in the absence of image logs and cores for characterizing SSFs and fractures; (b) to present an unconventional way of data conditioning using geostatistical and structural filtering; (c) to provide an advanced workflow through multi-attributes, neural networks, and ant-colony optimization (ACO) for the recognition of fracture networks; and (d) to identify the fault and fracture orientation parameters within the study area. Initially, a steering cube was generated, and a dip-steered median filter (DSMF), a dip-steered diffusion filter (DSDF), and a fault enhancement filter (FEF) were applied to sharpen the discontinuities. Multiple structural attributes were applied and shortlisted, including dip and curvature attributes, filtered and unfiltered similarity attributes, thinned fault likelihood (TFL), fracture density, and fracture proximity. These shortlisted attributes were computed through unsupervised vector quantization (UVQ) neural networks. The results of the UVQ revealed the orientations, locations, and extensions of fractures in the study area. The ACO proved helpful in identifying the fracture parameters such as fracture length, dip angle, azimuth, and surface area. The adopted workflow also revealed a small scale fault which had an NNW–SSE orientation with minor heave and throw. The implemented workflow of structural interpretation is helpful for the field development of the study area and can be applied worldwide in carbonate, sand, coal, and shale gas fields.

The detailed reservoir characterization was examined for the Central Indus Basin (CIB), Pakistan, across Qadirpur Field Eocene rock units. Various petrophysical parameters were analyzed with the integration of various cross-plots, complex water saturation, shale volume, effective porosity, total porosity, hydrocarbon saturation, neutron porosity and sonic concepts, gas effects, and lithology. In total, 8–14% of high effective porosity and 45–62% of hydrocarbon saturation are superbly found in the reservoirs of the Eocene. The Sui Upper Limestone is one of the poorest reservoirs among all these reservoirs. However, this reservoir has few intervals of rich hydrocarbons with highly effective porosity values. The shale volume ranges from 30 to 43%. The reservoir is filled with effective and total porosities along with secondary porosities. Fracture–vuggy, chalky, and intracrystalline reservoirs are the main contributors of porosity. The reservoirs produce hydrocarbon without water and gas-emitting carbonates with an irreducible water saturation rate of 38–55%. In order to evaluate lithotypes, including axial changes in reservoir characterization, self-organizing maps, isoparametersetric maps of the petrophysical parameters, and litho-saturation cross-plots were constructed. Estimating the petrophysical parameters of gas wells and understanding reservoir prospects were both feasible with the methods employed in this study, and could be applied in the Central Indus Basin and anywhere else with comparable basins.

Nowadays, there are significant issues in the classification of lithofacies and the identification of rock types in particular. Zamzama gas field demonstrates the complex nature of lithofacies due to the heterogeneous nature of the reservoir formation, while it is quite challenging to identify the lithofacies. Using our machine learning approach and cluster analysis, we can not only resolve these difficulties, but also minimize their time-consuming aspects and provide an accurate result even when the user is inexperienced. To constrain accurate reservoir models, rock type identification is a critical step in reservoir characterization. Many empirical and statistical methodologies have been established based on the effect of rock type on reservoir performance. Only well-logged data are provided, and no cores are sampled. Given these circumstances, and the fact that traditional methods such as regression are intractable, we have chosen to apply three strategies: (1) using a self-organizing map (SOM) to arrange depth intervals with similar facies into clusters; (2) clustering to split various facies into specific zones; and (3) the cluster analysis technique is used to identify rock type. In the Zamzama gas field, SOM and cluster analysis techniques discovered four group of facies, each of which was internally comparable in petrophysical properties but distinct from the others. Gamma Ray (GR), Effective Porosity(eff), Permeability (Perm) and Water Saturation (Sw) are used to generate these results. The findings and behavior of four facies shows that facies-01 and facies-02 have good characteristics for acting as gas-bearing sediments, whereas facies-03 and facies-04 are non-reservoir sediments. The outcomes of this study stated that facies-01 is an excellent rock-type zone in the reservoir of the Zamzama gas field.

The tight gas reserves in the Hangjinqi area are estimated at 700 × 109 m³. Since the exploration of the Hangjinqi, numerous wells are already drilled. However, the Hangjinqi remains an exploration area and has yet to become a gas field. Identifying a paleo-depositional framework such as braided channels is beneficial for exploration and production companies. Further, braided channels pose drilling risks and must be properly identified prior to drilling. Henceforth, based on the significance of paleochannels, this study is focused on addressing the depositional framework and sedimentary facies of the first member (P²x¹) of the lower Shihezi formation (LSF) for reservoir quality prediction. Geological modeling, seismic attributes, and petrophysical modeling using cores, logs, interval velocities, and 3D seismic data are employed. Geological modeling is conducted through structural maps, thickness map, and sand-ratio map, which show that the northeastern region is uplifted compared to northwestern and southern regions. The sand-ratio map showed that sand is accumulated in most of the regions within member-1. Interval velocities are incorporated to calibrate the acoustic impedance differences of mudstone and sandstone lithologies, suggesting that amplitude reflection is reliable and amplitude-dependent seismic attributes can be employed. The Root Mean Square (RMS) attribute confirmed the presence of thick-bedded braided channels. The results of cores and logging also confirmed the presence of braided channels and channel-bars. The test results of wells J34 and J72 shows that the reservoir quality within member-1 of LSF is favorable for gas production within the Hangjinqi area.