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Hydraulic fracturing (HF) has been used for years to enhance oil and gas production from conventional and unconventional reservoirs. HF in enhanced geothermal systems (EGS) has become increasingly common in recent years. In EGS, hydraulic fracturing creates a geothermal collector in impermeable or low-permeable hot dry rocks. Artificial fracture networks in the collector allow for a continuous flow of fluid in a loop connecting at least two wells (injector and producer). However, it is challenging to assess the fracability of geothermal reservoirs for EGS. Consequently, it is necessary to design a method that considers multiple parameters when evaluating the potential of geothermal development. This study proposes an improved fracability index model (FI) based on the influences of fracability-related geomechanical and petrophysical properties. These include brittle minerals composition, fracture toughness, minimum horizontal in-situ stress, a brittleness index model, and temperature effect to quantify the rock’s fracability. The hierarchical analytic framework was designed based on the correlation between the influencing factors and rock fracability. The results of the qualitative and quantitative approaches were integrated into a mathematical evaluation model. The improved fracability index model’s reliability was evaluated using well logs and 3D seismic data on low-permeable carbonate geothermal reservoirs and shale gas horizontal wells. The results reveal that the improved FI model effectively demonstrates brittle regions in the low-permeable carbonate geothermal reservoir and long horizontal section of shale reservoir. We divide the rock fracability into three levels: FI > 0.59 (the rock fracability is good); 0.59 > FI > 0.32 (the rock fracability is medium); and FI < 0.32, (the rock fracability is poor). The improved FI model can assist in resolving the uncertainties associated with fracability interpretation in determining the optimum location of perforation clusters for hydraulic fracture initiation and propagation in enhanced geothermal systems.

Gas-lift dual gradient drilling (DGD) is a solution for the complex problems caused by narrow drilling windows in deepwater drilling. Investigations are lacking on using oil-based drilling fluid in DGD, which is the principal novel idea of the present study. This research compares the results obtained from two new models with those of Standing’s correlations for solubility and bubble point pressure. Nitrogen was selected as the injection gas, then the PVT behavior of drilling fluid (oil/water/Nitrogen) in gas-lift DGD was evaluated and compared by coding in MATLAB. Then, these results were used to calculate the bottom hole pressure and finally investigate the optimization of injected gas flow rate. According to the achieved results, the Standing model has some errors in evaluating the PVT behavior of “Nitrogen and oil-based drilling fluids” and is not recommended for the mixtures in the gas-lift DGD. Regarding optimizing gas flow rate, a discrepancy was observed between pressure values obtained from the new models and the Standing model for the case of high liquid flow rates at low gas flow rates because of the difference in PVT parameters. The developed codes are deposited on an online data repository for future users. This study lays the foundation for better planning of drilling in deepwater drilling projects.

Some inter-salt shale reservoirs have high oil saturations but the soluble salts in their complex lithology pose considerable challenges to their production. Low-field nuclear magnetic resonance (NMR) has been widely used in evaluating physical properties, fluid characteristics, and fluid saturation of conventional oil and gas reservoirs as well as common shale reservoirs. However, the fluid distribution analysis and fluid saturation calculations in inter-salt shale based on NMR results have not been investigated because of existing technical difficulties. Herein, to explore the fluid distribution patterns and movable oil saturation of the inter-salt shale, a specific experimental scheme was designed which is based on the joint adaptation of multi-state saturation, multi-temperature heating, and NMR measurements. This novel approach was applied to the inter-salt shale core samples from the Qianjiang Sag of the Jianghan Basin in China. The experiments were conducted using two sets of inter-salt shale samples, namely cylindrical and powder samples. Additionally, by comparing the one-dimensional (1D) and two-dimensional (2D) NMR results of these samples in oil-saturated and octamethylcyclotetrasiloxane-saturated states, the distributions of free movable oil and water were obtained. Meanwhile, the distributions of the free residual oil, adsorbed oil, and kerogen in the samples were obtained by comparing the 2D NMR T1-T2 maps of the original samples with the sample heated to five different temperatures of 80, 200, 350, 450, and 600 °C. This research puts forward a 2D NMR identification graph for fluid components in the inter-salt shale reservoirs. Our experimental scheme effectively solves the problems of fluid composition distribution and movable oil saturation calculation in the study area, which is of notable importance for subsequent exploration and production practices.

Seismic data and nuclear magnetic resonance (NMR) data are two of the highly trustable kinds of information in hydrocarbon reservoir engineering. Reservoir fluids influence the elastic wave velocity and also determine the NMR response of the reservoir. The current study investigates different pore types, i.e., micro, meso, and macropores’ contribution to the elastic wave velocity using the laboratory NMR and elastic experiments on coal core samples under different fluid saturations. Once a meaningful relationship was observed in the lab, the idea was applied in the field scale and the NMR transverse relaxation time (T2) curves were synthesized artificially. This task was done by dividing the area under the T2 curve into eight porosity bins and estimating each bin’s value from the seismic attributes using neural networks (NN). Moreover, the functionality of two statistical ensembles, i.e., Bag and LSBoost, was investigated as an alternative tool to conventional estimation techniques of the petrophysical characteristics; and the results were compared with those from a deep learning network. Herein, NMR permeability was used as the estimation target and porosity was used as a benchmark to assess the reliability of the models. The final results indicated that by using the incremental porosity under the T2 curve, this curve could be synthesized using the seismic attributes. The results also proved the functionality of the selected statistical ensembles as reliable tools in the petrophysical characterization of the hydrocarbon reservoirs.

This paper presents a novel approach that aims to predict better reservoir quality regions from seismic inversion and spatial distribution of key reservoir properties from well logs. The reliable estimation of lithology and reservoir parameters at sparsely located wells in the Sawan gas field is still a considerable challenge. This is due to three main reasons: (a) the extreme heterogeneity in the depositional environments, (b) sand-shale intercalations, and (c) repetition of textural changes from fine to coarse sandstone and very coarse sandstone in the reservoir units. In this particular study, machine learning (ML) inversion algorithm was selected to predict the spatial variations of acoustic impedance (AI), porosity, and saturation. While trained in a supervised mode, the support vector machine (SVM) inversion algorithm performed effectively in identifying and mapping individual reservoir properties to delineate and quantify fluid-rich zones. Meanwhile, the Sequential Gaussian Simulation (SGS) and Gaussian Indicator Simulation (GIS) algorithms were employed to determine the spatial variability of lithofacies and porosity from well logs and core analyses data. The calibration of the detailed spatial variations from post-stack seismic inversion using SVM and wireline logs data indicated an appropriate agreement, i.e., variations in AI is related to the variations in reservoir facies and parameters. From the current study, it was concluded that in a highly heterogeneous reservoir, the integration of SVM and GIS algorithms is a reliable approach to achieve the best estimation of the spatial distribution of detailed reservoir characteristics. The results obtained in this study would also be helpful to minimize the uncertainty in drilling, production, and injection in the Sawan gas field of Pakistan as well as other reservoirs worldwide with similar geological settings.