Fractures play a vital role in reservoir transformation, but the distribution of faults and fractures is difficult to obtain by observing cores, which ultimately limits the effective development of gas dolomite reservoirs. We propose an integrated method that incorporates thin-section observations, three-dimensional (3D) seismic data, and image logs to interpret the distribution of faults and fractures of Cambrian Longwangmiao Carbonate Formation to predict potential development areas in the Moxi-Gaoshiti area of the Sichuan Basin, South China. Firstly, the faults were well interpreted by using the automatic tracking and 3D visualization technique based on the new seismic combination attribute of symmetry and ant tracking. Secondly, a comprehensive analysis was conducted using the thin sections, paleogeomorphology, and in situ test results to determine the fracture types (corrosion and structural fractures). The results help us to find potential sweet spot zones with good permeabilities, which is of great significance in reducing the risk of water production of drilled wells in the field development.

Shear velocity is an important parameter in pre-stack seismic reservoir description. However, in the real study, the high cost of array acoustic logging leads to lacking a shear velocity curve. Thus, it is crucial to use conventional well-logging data to predict shear velocity. The shear velocity prediction methods mainly include empirical formulas and theoretical rock physics models. When using the empirical formula method, calibration should be performed to fit the local data, and its accuracy is low. When using rock physics modeling, many parameters about the pure mineral must be optimized simultaneously. We present a deep learning method to predict shear velocity from several conventional logging curves in tight sandstone of the Sichuan Basin. The XGBoost algorithm has been used to automatically select the feature curves as the model’s input after quality control and cleaning of the input data. Then, we construct a deep-feed neuro network model (DFNN) and decompose the whole model training process into detailed steps. During the training process, parallel training and testing methods were used to control the reliability of the trained model. It was found that the prediction accuracy is higher than the empirical formula and the rock physics modeling method by well validation.

For the successful discovery and development of tight sand gas reserves, it is necessary to locate sand with certain features. These features must largely include a significant accumulation of hydrocarbons, rock physics models, and mechanical properties. However, the effective representation of such reservoir properties using applicable parameters is challenging due to the complicated heterogeneous structural characteristics of hydrocarbon sand. Rock physics modeling of sandstone reservoirs from the Lower Goru Basin gas fields represents the link between reservoir parameters and seismic properties. Rock physics diagnostic models have been utilized to describe the reservoir sands of two wells inside this Middle Indus Basin, including contact cement, constant cement, and friable sand. The results showed that sorting the grain and coating cement on the grain’s surface both affected the cementation process. According to the models, the cementation levels in the reservoir sands of the two wells ranged from 2% to more than 6%. The rock physics models established in the study would improve the understanding of characteristics for the relatively high V_p/V_s unconsolidated reservoir sands under study. Integrating rock physics models would improve the prediction of reservoir properties from the elastic properties estimated from seismic data. The velocity–porosity and elastic moduli-porosity patterns for the reservoir zones of the two wells are distinct. To generate a rock physics template (RPT) for the Lower Goru sand from the Early Cretaceous period, an approach based on fluid replacement modeling has been chosen. The ratio of P-wave velocity to S-wave velocity (V_p/V_s) and the P-impedance template can detect cap shale, brine sand, and gas-saturated sand with varying water saturation and porosity from wells in the Rehmat and Miano gas fields, both of which have the same shallow marine depositional characteristics. Conventional neutron-density cross-plot analysis matches up quite well with this RPT’s expected detection of water and gas sands.

The Tajik Basin is located on the eastern edge of the Central Asian segment of the Tethyan tectonic domain. The basin underwent intense tectonic transformation during the Himalayan period, resulting in complex structural styles, unclear original sedimentary characteristics and oil and gas geological conditions, and a complex process of oil and gas accumulation, which restricts the further evaluation of the basin’s exploration potential. Studying the Tajik Basin in the macro background of the Tethys tectonic domain, the tectonic sedimentary evolution of the Tethys tectonic domain has a significant effect on the basin’s tectonic evolution, sedimentary characteristics, and oil and gas accumulation conditions. The Tajik Basin has gone through four stages of tectonic evolution: the Late Permian to Triassic was the stage of back arc foreland basin; the Jurassic period was the stage of back arc extensional faulting depression; the Cretaceous–Paleogene period was the stage of depression basins; and the Neogene is the stage of the regenerated foreland basins. Through field geological surveys and analysis of outcrop samples, it has been determined that the Tajik Basin has developed three sets of source rocks: the Middle and Lower Jurassic, Cretaceous, and Paleogene. Among them, the organic matter abundance of the Middle and Lower Jurassic is relatively high, most of them are in the mature stage, and they are primarily gas-generating source rocks. The Cretaceous and Paleogene source rocks are mainly oil generating and in a low-mature state. There are four sets of reservoirs developed in the Tajik Basin: Middle-Upper Jurassic carbonate rocks, Lower Cretaceous clastic rocks, Upper Cretaceous carbonate rocks and Paleogene carbonate rocks. Comprehensive research shows that the Tajik Basin mainly develops three types of oil and gas reservoirs: Jurassic carbonate gas reservoirs, distributed in the southwestern Gissar Uplift and Surhan Depression in the western part of the basin; Paleogene carbonate reservoirs, distributed in the southern Vakhsh Depression and the eastern Kuliabu Depression; and multi layer–multi lithology oil and gas reservoirs, distributed in the northern Dushanbe Depression. The primary controlling factor for the three types of oil and gas reservoirs is tectonic movement, which forms traps and simultaneously reshapes the reservoirs, ultimately leading to effective accumulation of oil and gas. The distribution of oil and gas in the Tajik Basin is characterized by “west gas and east oil, west more and east less, west pre-salt and east post-salt, and pre-salt gas and post-salt oil”. Affected by the regional tectonic movements of the Tethys rich oil and gas tectonic domain, the basin has high-quality hydrocarbon source rocks, reservoirs, and cap rock conditions. The pre-salt Jurassic has the potential to form large natural gas reservoirs, while the post-salt Cretaceous and Paleogene still have further potential for exploration.