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Due to the complex mineral composition, low clay content, and strong heterogeneity of the mixed sedimentary shale in the Xinjiang Salt Lake Basin, the wettability characteristics of the reservoir and their influencing factors are not yet clear, which restricts the evaluation of oil-bearing properties and the identification of sweet spots. This paper analyzed mixed sedimentary shale samples from the Lucaogou Formation of the Jimsar Sag and the Fengcheng Formation of the Mahu Sag. Methods such as petrographic thin sections, X-ray diffraction, organic matter content analysis, and argon ion polishing scanning electron microscopy were used to examine the lithological and mineralogical characteristics, geochemical characteristics, and pore space characteristics of the mixed sedimentary shale reservoir. Alternating imbibition and nuclear magnetic resonance were employed to quantitatively characterize the wettability of the reservoir and to discuss the effects of compositional factors, lamina types, and pore structure on wettability. Research findings indicate that the total porosity, measured by the alternate imbibition method, reached 72% of the core porosity volume, confirming the effectiveness of alternate imbibition in filling open pores. The Lucaogou Formation exhibits moderate to strong oil-wet wettability, with oil-wet pores predominating and well-developed storage spaces; the Fengcheng Formation has a wide range of wettability, with a higher proportion of mixed-wet pores, strong heterogeneity, and weaker oil-wet properties compared to the Lucaogou Formation. TOC content has a two-segment relationship with wettability, where oil-wet properties increase with TOC content at low TOC levels, while at high TOC levels, the influence of minerals such as carbonates dominates; carbonate content shows an “L” type response to wettability, enhancing oil-wet properties at low levels (<20%), but reducing it due to the continuous weakening effect of minerals when excessive. Lamina types in the Fengcheng Formation significantly affect wettability differentiation, with carbonate-shale laminae dominating oil pores, siliceous laminae contributing to water pores, and carbonate–feldspathic laminae forming mixed pores; the Lucaogou Formation lacks significant laminae, and wettability is controlled by the synergistic effects of minerals, organic matter, and pore structure. Increased porosity strengthens oil-wet properties, with micropores promoting oil adsorption through their high specific surface area, while macropores dominate in terms of storage capacity. Wettability is the result of the synergistic effects of multiple factors, including TOC, minerals, lamina types, and pore structure. Based on the characteristic that oil-wet pores account for up to 74% in shale reservoirs (mixed-wet 12%, water-wet 14%), a wettability-targeted regulation strategy is implemented during actual shale development. Surfactants are used to modify oil-wet pores, while the natural state of water-wet and mixed-wet pores is maintained to avoid interference and preserve spontaneous imbibition advantages. The soaking period is thus compressed from 30 days to 3–5 days, thereby enhancing matrix displacement efficiency.

Different types of shale-oil sweet spots have developed and are vertically stacked in multiple layers of the Qingshankou Formation in the Changling Depression, southern Songliao Basin. Furthermore, this area lacks a classification standard in the optimization of its shale-oil sweet-spot area/layers. Through relevant tests of the region in question’s organic geochemistry, physical properties, oiliness, and pore structure, this paper investigates the formation elements of shale-oil sweet spots. In addition, summaries of its enrichment-controlling factors are given, and the classification standard and evaluation method for understanding the comprehensive sweet spots of the interbedded-type shale oil are then established. The interbedded-type shale oil is enriched in the Qingshankou I Member in the Changling Depression, and it has the features of medium-to-high maturity, the development of inorganic pores and micro-cracks, as well as higher oil saturation and better oil mobility. The sweet-spot enrichment is affected by lamina type, sedimentary facies, maturity, and sand–shale combinations. Both silty-laminated felsic shale and argillaceous-laminated felsic shale, which are developed in semi-deep lakes, are favorable shale lithofacies as they have excellent brittleness and oil mobility. The high maturity and the interbedded combination of sand and shale ensure the efficient production of shale oil, among which the pure-shale section issues a continuous contribution to the production process. Combined with oil testing, sweet-spot classification standards and a comprehensive evaluation of interbedded-type shale oil were established. An area of 639.2 km2 for the interbedded-type shale-oil sweet spots was preferred, among which type I (193 km2) belonged to the combination of “good shale and good siltstone interlayers adjacent”, and type II belonged to “good shale and medium siltstone interlayers adjacent” combination (which have long-term low and stable production prospects). The research provides theoretical guidance on the effective exploration and development of the shale oil of the Qingshankou Formation in the Changling Depression.

Lithology identification is the basis for sweet spot evaluation, prediction, and precise exploratory deployment and has important guiding significance for areas with low exploration degrees. The lithology of the shale strata, which are composed of fine-grained sediments, is complex and varies regularly in the vertical direction. Identifying complex lithology is a typical nonlinear classification problem, and intelligent algorithms can effectively solve this problem, but different algorithms have advantages and disadvantages. Compared were the three typical algorithms of Fisher discriminant analysis, BP neural network, and classification and regression decision tree (C&RT) on the identification of seven lithologies of shale strata in the lower 1st member of the Shahejie Formation (Es1L) of Raoyang sag. Fisher discriminant analysis method is linear discriminant, the recognition effect is poor, the accuracy is 52.4%; the accuracy of the BP neural network to identify lithology is 82.3%, but it belongs to the black box and can not be visualized; C&RT can accurately identify the complex lithology of Es1L, the accuracy of this method is 85.7%, and it can effectively identify the interlayer and thin interlayer in shale strata.

In order to analyze the main factors controlling shale gas accumulation and to predict the potential zone for shale gas exploration, the heterogeneous characteristics of the source rock and reservoir of the Wufeng-Longmaxi Formation in Sichuan Basin were discussed in detail, based on the data of petrology, sedimentology, reservoir physical properties and gas content. On this basis, the effect of coupling between source rock and reservoir on shale gas generation and reservation has been analyzed. The Wufeng-Longmaxi Formation black shale in the Sichuan Basin has been divided into 5 types of lithofacies, i.e., carbonaceous siliceous shale, carbonaceous argillaceous shale, composite shale, silty shale, and argillaceous shale, and 4 types of sedimentary microfacies, i.e., carbonaceous siliceous deep shelf, carbonaceous argillaceous deep shelf, silty argillaceous shallow shelf, and argillaceous shallow shelf. The total organic carbon (TOC) content ranged from 0.5% to 6.0% (mean 2.54%), which gradually decreased vertically from the bottom to the top and was controlled by the oxygen content of the bottom water. Most of the organic matter was sapropel in a high-over thermal maturity. The shale reservoir of Wufeng-Longmaxi Formation was characterized by low porosity and low permeability. Pore types were mainly <10 nm organic pores, especially in the lower member of the Longmaxi Formation. The size of organic pores increased sharply in the upper member of the Longmaxi Formation. The volumes of methane adsorption were between 1.431 m3/t and 3.719 m3/t, and the total gas contents were between 0.44 m3/t and 5.19 m3/t, both of which gradually decreased from the bottom upwards. Shale with a high TOC content in the carbonaceous siliceous/argillaceous deep shelf is considered to have significant potential for hydrocarbon generation and storage capacity for gas preservation, providing favorable conditions of the source rock and reservoir for shale gas.

The pore structure of shale plays key role in oil and gas storage capacity and accumulation. Twelve representative samples were selected from Triassic Yanchang Formation in the Ordos Basin and Silurian Longmaxi Formation in the Sichuan Basin with different ages, depositional settings, and maturities to analyze shale pore structure using focused ion beam-scanning electron microscopy and high-pressure mercury intrusion capillary porosimetry. The results show that the pores of lacustrine shale with maturity Ro < 1.3% from the Triassic Yanchang Formation were predominantly composed of pores with pore throat diameter of larger than 30 μm. The pores of marine shale with maturity Ro > 1.3% from the Silurian Longmaxi Formation were predominantly composed of pores with pore throat diameter of smaller than 100 μm. For the porosity, the average porosity of low-mature lacustrine shale is 2.4%, while the average porosity of high-mature marine shale is 1.5%. For the pore type, intergranular inorganic pores predominantly occurred between mineral particles in the lacustrine shale, while the marine shale mainly developed organic pores with pore throat diameters ranging from 5 to 200 nm. Compared to the low-mature lacustrine shale, macropores of high-mature marine shale are less developed and micropores dominant. Importantly, brittle minerals (quartz, feldspar, and carbonate minerals) mainly affect the pore structure of lacustrine shale, while organic matter mainly affects the pore structure of marine shale.

Due to diverse mineral composition and complex lithology, mixed shale oil reservoir wettability features and the associated controlling factors remain unclear, limiting the evaluation of the oil-bearing property and sweet spot distribution. This paper examined mixed shale oil samples from the Jimusar Sag in the Lucaogou Formation. Casting thin sections, X-ray diffraction, geochemical characteristics, argon ion polishing scanning electron microscope, and high pressure mercury injection were used to analyze lithologic characteristics, mineral compositions, and pore-throat structures within the mixed shale oil reservoir. Using spontaneous imbibition and nuclear magnetic resonance, the wettability characteristics were analyzed. Impacts of organic matter abundance, mineral composition, pore-throat structure, and source-reservoir combination on wettability were discussed. The Lucaogou mixed shale oil reservoir primarily contains intergranular dissolved pores, intragranular dissolved pores, and intercrystalline pores. Three types of mercury intrusion cures were observed, including a weak platform shape (type I), gentle straight line shape (type II), and upward convex shape (type III), corresponding to intergranular, dissolved, and dissolved-intercrystalline dominant pore-throat systems, respectively. Mixed shale oil reservoirs show dual wet properties, containing both oil-wet and water-wet pores. Oil-wet pores (large pores with T2>1 ms) are more common and have better connectivity than water-wet pores (small pores with T2<1 ms). Dolomite-bearing siltstone and mudstone are primarily strongly oil-wet, while siltstone is primarily mixed wet. Type II and type III pore-throat systems are more oil-wet than type I for the same source-reservoir combination. Mixed shale oil reservoir wettability is primarily controlled by three factors, including organic matter abundance, source-reservoir combination, and dolomite content. The influence of the pore-throat structure is weak. High organic matter abundance, an integrated source-reservoir or adjacent source-reservoir, and appropriate dolomite content are necessary conditions for forming a strong oil-wet shale oil reservoir in the Lucaogou Formation. Stronger oil-wet is beneficial for shale oil charging and enrichment.

As significant components of tight gas reservoirs, clay minerals with ultrafine dimensions play a crucial role in controlling pore structures and permeability. XRD (X-ray diffraction), SEM (scanning electron microscopy), N2GA (nitrogen gas adsorption), and RMIP (rate-controlled mercury injection porosimetry) experiments were executed to uncover the effects of clay minerals on pore structures and the permeability of tight gas reservoirs, taking tight rock samples collected from the Lower Cretaceous Dengloukou and Shahezi Formations in the Xujiaweizi Rift of the northern Songliao Basin as an example. The results show that the pore space of tight gas reservoirs primarily comprises intragranular-dominant pore networks and intergranular-dominant pore networks according to fractal theory and mercury intrusion features. The former is interpreted as a conventional pore-throat structure where large pores are connected by wide throats, mainly consisting of intergranular pores and dissolution pores, and the latter corresponds to a tree-like pore structure in which the narrower throats are connected to the upper-level wider throats like tree branches, primarily constituting intercrystalline pores within clay minerals. Intragranular-dominant pore networks contribute more to total pore space, with a proportion of 57.79%–90.56%, averaging 72.55%. However, intergranular-dominant pores make more contribution to permeability of tight gas reservoirs, with a percentage of 62.73%–93.40%. The intragranular-dominant pore networks gradually evolve from intergranular-dominant pore networks as rising clay mineral content, especially authigenic chlorite, and this process has limited effect on the total pore space but can evidently lower permeability. The specific surface area (SSA) of tight gas reservoirs is primarily derived from clay minerals, in the order of I/S (mixed-layer illite/smectite) > chlorite > illite > framework minerals. The impact of clay minerals on pore structures of tight gas reservoirs is correlated to their types, owing to different dispersed models and morphologies, and chlorite has more strict control on the reduction of throat radius of tight rocks.

The effects of high debris content on pore structure in tight sandstone reservoirs tight sandstone reservoirs are multifaceted. Pore structure is an important factor controlling reservoir quality. Clarifying the effects of different types of rock debris on reservoirs is necessary to study the pore structure and their control factors of tight sandstones. The Western Sichuan Depression with complex rock components, containing multiple types of rock debris, leads to strong heterogeneity of pore throats, so it is necessary to study the factors controlling the development of different types of pore throats in tight reservoirs. In this paper, the Fourth member of Xujiahe Formation (T3x4) is taken as the research object. Based on high-pressure mercury intrusion experiments and the fractal theory, the types of pore throats and their heterogeneity in tight reservoirs were studied, the relationship of fractal dimensions with reservoir physical properties, pore structure, and rock compositions were investigated, and then the controlling factors for the development of different types of pore throats are clarified. The studies show that there are four types of pore throats developed in the T3x4 of the western Sichuan depression, including primary intergranular pore-throats (>350 nm), residual intergranular pore-throats (75–350 nm), dissolution pore-throats (16–75 nm), and intercrystalline pore-throats (<16 nm), among which the homogeneity of dissolution pore-throats are the best, followed by residual intergranular pore-throats and intercrystalline pore-throats, and the primary intergranular pore-throats the most heterogeneous. The permeability has a better relationship with the proportion and fractal dimension of primary intergranular pore-throats and residual intergranular pore-throats of tight reservoir of the Xujiahe Formation. The relation-ship between porosity and the proportion and fractal dimension of primary intergranular pore-throats and dissolution pore-throats is better. Brittle minerals such as quartz and metamorphic debris, as well as early developed films of chlorite and illite mainly control the development of intergranular pore-throats. Potassium feldspar mainly controls the development of dissolution pore-throats, while sedimentary rock debris, volcanic debris, and kaolinite play a destructive role for all types of pore-throats. The high-quality reservoirs in the T3x4 are controlled by the development of primary intergranular pore throats and dissolution pore throats, and they are mainly developed in environments with strong hydrodynamic conditions, large rock grain sizes, high content of brittle minerals such as quartz and metamorphic debris, extensive development of chlorite and illite films, and low content of sedimentary rock debris, matrix, and cemented materials. This study is of guiding significance in clarifying the causes of heterogeneity in different types of pore-throat systems in tight sandstones and the formation mechanism of high-quality reservoirs in tight sandstones with high content of debris.

Machine learning is the main technical means for lithofacies logging identification. As the main target of shale oil spatial distribution prediction, mud shale petrography is subjected to the constraints of stratigraphic inhomogeneity and logging information redundancy. Therefore, choosing the most applicable machine learning method for different geological characteristics and data situations is one of the key aspects of high-precision lithofacies identification. However, only a few studies have been conducted on the applicability of machine learning methods for mud shale petrography. This paper aims to identify lithofacies using commonly used machine learning methods. The study employs five supervised learning algorithms, namely Random Forest Algorithm (RF), BP Neural Network Algorithm (BPANN), Gradient Boosting Decision Tree Method (GBDT), Nearest Neighbor Method (KNN), and Vector Machine Method (SVM), as well as four unsupervised learning algorithms, namely K-means, DBSCAN, SOM, and MRGC. The results are evaluated using the confusion matrix, which provides the accuracy of each algorithm. The GBDT algorithm has better accuracy in supervised learning, while the K-means and DBSCAN algorithms have higher accuracy in unsupervised learning. Based on the comparison of different algorithms, it can be concluded that shale lithofacies identification poses challenges due to limited sample data and high overlapping degree of type distribution areas. Therefore, selecting the appropriate algorithm is crucial. Although supervised machine learning algorithms are generally accurate, they are limited by the data volume of lithofacies samples. Future research should focus on how to make the most of limited samples for supervised learning and combine unsupervised learning algorithms to explore lithofacies types of non-coring wells.