Shanxi Formation Shan 2³ Submember transitional shale in Eastern Ordos Basin is characterized by high TOC value, wide distribution, and large single-layer/cumulative thickness. In this study, based on section division of Shan 2³ Submember, petrographic, mineralogical, and high-resolution geochemical analyses were integrated to reveal sedimentary environment, detrital influx, paleoclimate, paleosalinity, and paleoredox conditions. Results indicate that Shan 2³ Submember is divided into four sections (Shan 2³-1, Shan 2³-2, Shan 2³-3, and Shan 2³-4). The upper part of the Shan 2³-1 section is dominated by bay facies, which is characterized by high TOC value (2.75%–10.96%, avg. 6.98%), low detrital influx proxies (Zr, 97–527 ppm, avg. 310 ppm; Ti, 1985–7591 ppm, avg. 3938 ppm), relatively dry paleoclimate condition (CIA^*, 41.96–92.58, avg. 75.55; Sr/Cu, 6.23–14.49, avg. 8.87), high paleosalinity proxies (Sr/Ba, 0.39–1.29, avg. 0.62), and relatively anoxic reduction condition (U_EF, 0.83–3.00, avg. 1.67; Mo_EF, 3.95–27.00, avg. 15.56). By comparison, the other three sections are dominated by a combination of transitional facies, including barrier island, lagoon, tidal flat, and swamp. In this interval, shale is deposited in lagoon facies. The paleoclimate gradually tends to be warm and humid, which results in increasing the chemical weathering intensity. Meanwhile, the detrital influx increases, and the paleoredox condition tends to be oxic. The above conditions are not conducive to the preservation of organic matter in lagoon facies shale.

The lacustrine shale, represented by the Lianggaoshan Formation, is widely distributed in oil and gas basins of China and will be a key target for unconventional hydrocarbon exploration in the future. Due to the complexity of geological conditions, the distribution of lacustrine shale and the mechanism of organic matter (OM) enrichment show significant differences between different basins. In this study, seismic interpretation, core observation, high-frequency geochemical analysis and other methods are integrated to reveal factors controlling lacustrine shale distribution and OM accumulation in lacustrine shale. The results suggest that six bottom-to-top organic-rich shale intervals are identified within the Lianggaoshan Formation due to lake-basin migration. The migration process of depocenters controls the planar distribution of lacustrine organic-rich shale. The organic-rich lacustrine shale within 1st Member and 2nd Member is characterized by relatively high paleoproductivity and dysoxic condition. The lacustrine organic-rich shale of the upper to the top of 3rd Member is characterized by relatively low paleoproductivity, relatively high terrestrial input, and dysoxic condition. Paleoproductivity and preservation condition caused by lake-level rise are generally the major influencing factor of organic matter accumulation in 1st Member and 2nd Member organic-rich shale. The input of terrestrial OM, and the condition of preservation caused by rapid deposition are the major factors controlling OM accumulation in 3rd Member of Lianggaoshan Formation.

In order to define a favorable oil and gas accumulation area, this study focused on reservoir recognition which is based on logging data of old wells. The Gaofengchang structure in eastern Sichuan is used as a test area to discuss the necessity and feasibility of curve construction by combining new and old wells. Analysis of the reasons for the inaccuracy of the traditional curve reconstruction method is also provided. Given the interdependence of the well log in the depth domain sample sequence, a new intelligent construction method (BI-LSTM) based on the cyclic neural network is proposed. A discussion on the effect of data increments on prediction accuracy is also provided. The following four conclusions were achieved through curve reconstruction experiments: a high-precision CNL pseudo-curve was obtained; an underdetermined equation in optimization logging interpretation method needed to be extended to a positive definite equation; the quantitative processing of the complex lithologic reservoir parameters for the old wells was realized; and the processing result of the lithology physical property were basically consistent with the core data. Therefore, the BI-LSTM proposed in this paper could improve the accuracy of logging curve construction and has a good promotion significance for the old well review.

Summary: Liquid loading occurs when gas production falls below the critical liquid-carrying flow rate of the gas well, resulting in the inability to remove the condensate or water in the gas well. Liquid loading can lead to a sharp reduction in production, which affects the gas well ultimate recovery. Accurate prediction of the timing of liquid loading is important for implementing mitigations that reduce liquid accumulation in the production tubing and prevent gas production impairment, as well as for the stability of production. Existing liquid-loading forecasting methods have a time offset in the determination of liquid loading, and there is great variation in the results for different gas wells. Currently, supervisory control and data acquisition (SCADA) systems are widely used for gas well production data acquisition, but the data are not effectively utilized. Deep machine learning techniques are applied to the field data from gas wells and have achieved significant effectiveness. In this study, a bidirectional long short-term memory network (Bi-LSTM) was used to conduct feature extraction on the production data, and the extracted feature was spliced together with the geological and engineering parameter feature. These features were combined with self-attention mechanisms to predict the time of the next liquid loading. Because the modeling results fit the actual liquid loading in production scenarios better, our method also customizes the loss functions. Experimental verification was conducted using actual production data from 13 gas wells. The recall was 1 and F1 was 0.87 for the experimental data in the model, and the customized loss function led to a 6.5% improvement in F1. The experimental results verify that our method can accurately forecast liquid-loading onset in gas wells in a timely manner, which can help reduce costs and increase efficiency in shale gas production.

Deeply buried (>3500 m) marine shale has become a focus point for the future exploration and exploitation of shale hydrocarbon in China. Low-temperature nitrogen adsorption (LTNA), scanning electron microscopy (SEM), nuclear magnetic resonance (NMR), and other experiments were combined to characterize the pore structure and fluid division in deep-marine shale of the southern Sichuan Basin in this study. The results suggest that the deep-marine shale had a relatively developed nanopore network, especially with honeycomb organic pores. These organic pores were largely macropores with good connectivity in three-dimensional space and constituted the major reservoir space of the deep-marine shale gas. Microfractures were predominantly clay-mineral-related fractures, and the development degree of microfractures connected with organic pores was low, which contributed to the preservation of organic pores. Within the deep-marine shale interval, the pore volumes of Section 1 and Section 3 were higher. Pore volume was predominantly contributed by pores above 10 nm, where macropores accounted for a large proportion. Based on a combination of high-speed centrifugation and gradient temperature drying, the pore fluid of deep-marine shale reservoirs was quantitatively classified into four types: clay-bound fluid, capillary-bound fluid, free-flowing fluid, and closed-pore fluid. The clay-bound fluid existed in pores of less than 4.25 nm, which cannot be exploited. Quantitative division of the shale pore system could be realized by using the pore space differences of different types of fluids.