ABSTRACT The Aptian pre‐salt lacustrine carbonates represent the most significant oil exploration target along the South Atlantic margin. Dolomitization of the Santos Basin Barra Velha Formation (BVF) is critical for reservoir quality, yet its origin, patterns, and evolution remain poorly understood. This study characterizes the petrographic features of core samples from the Sururu field, identifying four distinct dolomite types—blocky, lamellar, spheroidal, and saddle—within the diagenetic framework of the BVF and evaluating their impact on reservoir quality. Dolomite precipitation occurred predominantly during eodiagenesis through multiple pathways, mainly involving the replacement of Mg‐rich clays or cementing secondary pores. Blocky dolomite formed mostly through consumption of Mg‐clay precursors, evolving from scattered floating rhombs to coalescent mosaic textures. Spheroidal dolomite is associated with the early replacement of peloidal Mg‐clays, whereas lamellar dolomite filled shrinkage pores within the laminated clay matrix. Saddle dolomite represents a later‐stage cement, commonly linked to hydrothermal fluids. Scanning electron microscopy (SEM)–energy‐dispersive spectrometer (EDS) data indicate Ca‐enriched, nonstoichiometric compositions, with Mg mol fractions ranging from ∼0.37 to 0.45, as well as the presence of Si–Al‐rich clay remnants within crystals, supporting a close association between dolomite formation and Mg‐clay precursors. These different dolomite fabrics exert contrasting effects on reservoir properties. Blocky crystals occurring in early, partially coalescent stages, as well as lamellar dolomite associated with shrinkage pores, tend to coexist with matrix‐dissolution porosity and display a wide range of permeability values. In contrast, the development of mosaic blocky dolomite and late saddle cement significantly reduce both porosity and permeability. This study enhances the understanding of dolomite formation in alkaline lacustrine systems and provides valuable insights into reservoir quality and heterogeneity in the pre‐salt succession.
ABSTRACT Faults are well developed in the Lishu Depression, and their transport capacity strongly governs differential hydrocarbon enrichment. However, the phases and boundaries of hydrocarbon migration along these faults remain poorly constrained, hindering a quantitative evaluation of fault transport capacity. In this study, well‐logging parameters were used to characterize fault zone architectures. Microscopy, fluid inclusion analysis, and bitumen Raman spectroscopy were performed on fault zone samples to identify internal hydrocarbon migration pathways and phases and ultimately to establish threshold activity rates for distinct transport stages. The Lishu Depression hosts two types of fault zone architecture: one‐unit (damage zone only) and two ‐ unit (fault core and damage zone). The damage zone exhibits high fracture density and large pore‐throat radii, resulting in more favorable flow conditions than the fault core and protolith and thus serves as the principal hydrocarbon transporting pathway. Observations of bitumen and hydrocarbon inclusions within the fault zone reveal that asphaltic bitumen formed from migration differentiation; its formation temperature is consistent with the homogenization temperatures of aqueous fluid inclusions coeval with hydrocarbon inclusions. Both indicate that the main phase of hydrocarbon migration along the fault zone occurred during the deposition of the Quantou Formation. On the basis of the fault activity rate corresponding to this migration period, the threshold activity rate between the intermittent and static periods is determined to be 1.5 m/Ma. Furthermore, on the basis of the relationship between fault activity rate and the hydrocarbon fullness degree of associated traps, the threshold between the active and intermittent periods is 4.9 m/Ma.
ABSTRACT Accurate identification of gas‐hydrate‐bearing layers in permafrost regions is critical for gas‐hydrate exploration and development. Conventional well‐logging methods are often affected by lithology, pore structure, and other interfering factors, leading to complex and ambiguous responses that hinder precise evaluation of gas‐hydrate saturation. Machine learning models offer an automated approach to well‐logging interpretation, improving efficiency and reducing dependence on expert knowledge. In this study, four models—Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), support vector machine (SVM), and K ‐nearest neighbors ( K NN)—are optimized using the grey wolf optimizer (GWO) algorithm and subsequently compared. The results demonstrate that the GWO–LightGBM model achieves an overall identification accuracy of 97.14%. For the gas‐hydrate class (the positive class), it yields an F 1‐score of 84.82%. The proposed model significantly outperforms the other three comparison models. Furthermore, validation using independent well data and SHAP (SHapley Additive exPlanations) analysis confirms the excellent generalization ability and interpretability of the proposed model.
ABSTRACT In marginal‐marine nearshore–shelf systems, sedimentary environments constrain sand–mud zonation and ichnofabric development, both of which are associated with spatial variations in reservoir quality. This study focuses on the Neogene strata of the Songtao Uplift on the northern margin of the Qiongdongnan Basin. By integrating cores, thin section, electrical micro‐resistivity imaging (ERMI) logs, conventional logs, and porosity–permeability data, a multi‐proxy facies indicator system was established to delineate sedimentary microfacies and construct a sedimentary facies model. On this basis, reservoir‐quality variations were evaluated. Six sedimentary microfacies were identified: upper nearshore, middle nearshore, lower nearshore, inner shelf, outer shelf, and turbidite depositional facies. The study area is dominated by a nearshore–shelf sedimentary system, with episodic turbidite deposits developed locally. Reservoir quality shows a systematic association with facies‐dependent sand–mud architecture and ichnofabric geometry. Sand‐rich nearshore intervals generally exhibit higher porosity and permeability than mud‐rich shelf intervals, with the upper nearshore representing the most favorable reservoir belt. The lower nearshore can locally retain relatively favorable properties where sandy beds coincide with Planolites – Thalassinoides ichnofabrics characterized by combined linear and reticulate burrow geometries, whereas the inner and outer shelf microfacies are generally characterized by low porosity and permeability. Accordingly, the “facies‐belt framework + burrow‐connectivity potential” approach is proposed as a hierarchical geological screening framework for identifying potentially favorable bioturbated intervals in analogous marginal‐marine nearshore–shelf systems.
ABSTRACT The Paleogene formations in the Huizhou 19‐6 area are dominated by high sand‐content strata. Fault development in this region is controlled by multiple episodes of tectonic activity, forming a structural pattern of ‘initially wrench‐dominated, followed by extensional deformation’. Under these conditions, faults play a dual role as both conduits and seals during hydrocarbon migration and accumulation. Accurate assessment of fault sealing capacity is therefore critical for understanding deep reservoir formation patterns and optimizing exploration well placement. Traditional evaluation methods based on shale smear zones are unsuitable for sand‐rich formations, as they fail to capture the mechanical essence of fault sealing. Nevertheless, field observations confirm that faults in these sand‐rich layers can exhibit strong sealing properties. This study adopts a geomechanical approach focused on faults within the high‐sand‐content strata of the Huizhou 19‐6 area. We integrate three‐dimensional (3D) seismic data, geomechanical experiments, imaging logging data and finite element numerical simulations to construct a geomechanical model for fault zone sealing capacity evaluation. Particular emphasis is placed on how multistage stress reorientation affects fault sealing behaviour. The results show that repeated changes in in situ stress orientation alter the shear and normal stress states along fault planes. In sand‐rich formations, this leads to stronger sealing in northwest‐trending faults than in nearly east–west‐trending faults. In the absence of stress reorientation, sustained extensional forces increase the slip tendency index of northwest‐trending faults, thereby weakening their sealing capacity. These findings provide theoretical support and technical guidance for deep hydrocarbon exploration in the Huizhou 19‐6 area and similar structural settings within the Pearl River Mouth Basin. They also offer significant reference value for trap evaluation and well placement in offshore deep clastic reservoirs.
ABSTRACT Hydrocarbon reservoir is conventionally treated as a single rock unit, which adversely affects productivity. In most cases, this approach is unrealistic in complex geologic environment where reservoirs have several components that contribute to the flow of hydrocarbon. This study delineates reservoir rocks into its various rock types and flow units in “D” field, offshore Niger Delta. Suites of well log (gamma ray, resistivity, sonic, density, and neutron) from three wells and core data were utilized. Conventional method of flow unit analysis, involving flow zone indicator (FZI) approach, rock physics, and K ‐means clustering was adopted for the study. Two reservoirs, R1 and R2, were delineated, and their petrophysical properties were also computed. Petrophysical parameters showed that R1 has a gross thickness ranging from 7 to 14 m, whereas R2 ranged from 2 to 7 m. In R1, ( V sh ) varied from 0.13 to 0.30, porosity ranged from 0.20 to 0.31, permeability varied from 1402 to 2947 mD, and hydrocarbon saturation ranged from 0.10 to 0.47. In R2, V sh ranges from 0.17 to 0.32, porosity varied from 0.19 to 0.25, permeability ranged from 1247 to 2392 mD, and hydrocarbon saturation varied from 0.26 to 0.74. Three rock types, namely, poorly sorted unconsolidated sand, well‐sorted unconsolidated sand, and moderately cemented sand, were determined from compressional velocity ( V p ) against porosity crossplot, superimposed on rock physics models. The well‐sorted and moderately cemented sandstones retained reservoir potential despite increased acoustic velocities, whereas the poorly sorted unconsolidated sands represent non‐reservoir facies due to poor consolidation and elevated clay content. Rock physics‐based K ‐means clustering identified four flow units in Reservoir R1 and three in Reservoir R2. Using FZI‐based K ‐means clustering, three hydraulic flow units were established in both reservoirs, which supports the rockphysics findings. FU1 exhibits the best reservoir quality. The integrated workflow has reliably characterized flow units in both cored and uncored reservoirs, thereby reducing reservoir uncertainty and supporting hydrocarbon recovery.
ABSTRACT This study evaluates the simultaneous co‐optimization of carbon dioxide storage and enhanced oil recovery (CO 2 ‐EOR) in the X Oilfield, Cuu Long Basin, Vietnam, to assess long‐term CO 2 storage integrity over 300 years. A dynamic reservoir model was developed from a geological model constructed in Petrel and validated using ECLIPSE simulation, achieving approximately 90% accuracy against historical production data. On the basis of the validated model, sensitivity analysis was conducted to investigate the impact of key operational parameters on CO 2 ‐EOR performance and CO 2 sequestration. The results indicate that a CO 2 injection rate of 20,000 Mscf/day maximizes oil recovery. In contrast, a higher injection rate of 40,000 Mscf/day yields the most significant economic benefit, driven by carbon credit incentives and government subsidies. To address the computational cost and time limitations of conventional numerical simulation, a surrogate reservoir model (SRM) based on long short‐term memory (LSTM) neural networks was developed. The SRM model rapidly produces forecasting and facilitates uncertainty quantification through 100 Monte Carlo realizations. Probabilistic results confirm the project's technical and economic feasibility, with a total CO 2 storage capacity of approximately 16.1 million tons. Long‐term simulations extending to the year 2300 demonstrate stable CO 2 plume migration and effective geological containment, confirming the viability of the proposed co‐optimization strategy for oilfields approaching late‐stage production.
ABSTRACT CO 2 foam flooding is a promising chemical enhanced oil recovery method for improving gas mobility control and sweep efficiency in sandstone reservoirs, but foam instability remains a major limitation. In this study, a green hybrid CO 2 foam system was developed using powder‐extracted cottonseed surfactant (PECS) as a bio‐derived anionic surfactant and olive‐oil‐derived carbon nanodots (CNDs) as nanoscale stabilizing additives. CNDs were synthesized through a hydrothermal route and characterized by attenuated total reflectance–Fourier transform infrared spectroscopy (ATR–FTIR), ultraviolet–visible (UV–Vis) spectroscopy, and transmission electron microscopy (TEM). Static foam stability, bubble morphology, high pressure and high temperature (HPHT) interfacial tension (IFT), and tertiary core‐flooding tests were conducted to evaluate the PECS–CND system. Among the tested PECS concentrations, 1200 ppm showed the highest foam half‐life of 120 min and the lowest IFT, confirming its optimum performance near the critical micelle concentration. Adding 100 ppm CND increased the foam half‐life from 120 to 125 min and increased t 95 from 540 to 840 s, indicating delayed drainage and improved time‐dependent foam stability. Bubble morphology analysis showed that the PECS–CND foam retained a measurable and compact texture for a longer aging period than PECS alone. At 80°C and 1000 psi, CND addition reduced the IFT from 0.324 to 0.304 mN/m. Core flooding showed that PECS–CND CO 2 foam provided 22.6% original oil in place (OOIP) incremental recovery after waterflooding and increased the final recovery to approximately 81.5% OOIP. These findings demonstrate that combining a bio‐derived anionic surfactant with green CNDs can improve CO 2 foam persistence, support gas mobility control, and enhance residual oil mobilization in sandstone porous media. The results suggest that the PECS–CND system improves oil recovery through combined interfacial activity, lamella reinforcement, delayed drainage/coalescence, and CO 2 mobility control.
ABSTRACT Volcanic ash input and lake‐bottom hydrothermal activity are commonly invoked to explain organic‐rich source‐rock formation, yet their respective geochemical signals and effects on organic matter enrichment remain difficult to distinguish in lacustrine basins. This study investigates the Triassic Yanchang Formation (Chang 7 Member) in the Ordos Basin using petrographic observations, scanning electron microscopy, organic geochemistry, and major‐ and trace‐element data from source rocks and tuffs. The results show that tuff occurrence and enrichments of Th, Pb, and Zr record the relative influence of volcanic input, whereas Mo and Cu enrichment, together with siliceous rocks and pyrite‐bearing hydrothermal‐associated textures, indicate lake‐bottom hydrothermal influence. The main innovation of this study is to separate the volcanic and hydrothermal signals and to evaluate their different roles in source‐rock development. Volcanic input appears to promote organic matter enrichment mainly in samples with total organic carbon (TOC) below 10%, probably through nutrient supply, but its effect weakens or becomes negative in high‐TOC black shales. In contrast, hydrothermal influence is more closely associated with high‐TOC source rocks, enhanced paleoproductivity, and reducing bottom‐water conditions. These findings provide refined criteria for distinguishing volcanic and hydrothermal effects in the Chang 7 Member and help explain spatial differences in organic‐rich source‐rock development in the Ordos Basin.
ABSTRACT Cation‐exchange capacity per volume (QV) plays a pivotal role in reservoir engineering, influencing ion transport, wettability alteration, and enhanced oil recovery (EOR) processes. This study introduces a gradient boosting machine (GBM) framework integrated with four optimization strategies, including evolution strategies (ES), batch Bayesian optimization (BBO), Bayesian probability improvement (BPI), and Gaussian process optimization (GPO), to achieve robust characterization of QV in oil and gas applications. Comparative evaluation based on test‐stage performance demonstrated consistently high accuracy, with GBM‐ES and GBM‐BPI achieving superior R 2 values (0.987) and low mean squared errors (0.00062 and 0.00063, respectively). GBM‐BPI further minimized average absolute relative error (2.776%), whereas GBM‐ES balanced accuracy with computational efficiency. Runtime analysis revealed trade‐offs among optimizers, with GPO converging fastest (532.5 s) compared to BBO's extended runtime (1537.9 s). SHapley Additive exPlanations (SHAP)‐based interpretability and sensitivity analysis provided transparent insights into feature contributions, identifying the dominant reservoir parameters governing QV variation. By emphasizing test‐stage performance and interpretability, this framework establishes a reproducible methodology for QV assessment, offering a scalable tool for reservoir characterization, ion‐exchange modeling, and optimization of EOR strategies in the petroleum industry.
ABSTRACT Predicting source rock quality from geochemical proxies is fundamental to shale gas exploration, yet conventional approaches provide point estimates without quantifying prediction confidence. We develop and validate ensemble machine learning models for predicting total organic carbon (TOC) and source rock quality across the Lower Cambrian Qiongzhusi Formation, using 1264 core samples from 12 wells spanning 5 depositional settings, and introduce conformal prediction to deliver calibrated uncertainty intervals. Random Forest regression performance is reported across three operationally distinct configurations: trace‐element geochemistry alone predicts TOC at R 2 = 0.77 (MAE = 0.95 wt%), incorporating depositional setting raises this to R 2 = 0.88 (MAE = 0.71 wt%), and the four‐feature reduced model (Mo EF, V/(V + Ni), S, depositional setting) recovers R 2 = 0.87 (MAE = 0.74 wt%) from operationally minimal inputs. The reduced model is recommended as the operational tool for routine exploration screening. The full model substantially outperforms multiple linear regression ( R 2 = 0.78). Vanadium concentration is the strongest single predictor (impurity importance 50.4% and permutation importance 36.4%), with the contrast against the V/(V + Ni) ratio (0.6%) explained by the Mo reservoir effect in restricted‐basin settings. Conformal prediction provides calibrated uncertainty intervals: the 90% prediction interval is ±1.87 wt% TOC with empirical coverage of 90.0%, validated under both out‐of‐fold and strict two‐stage split procedures. Uncertainty varies systematically with depositional setting—from ±0.50 wt% on the platform to ±2.37 wt% in basin center settings. Binary classification at the operationally relevant TOC ≥ 2 wt% threshold for shale‐gas prospectivity achieves 98.5% accuracy. Leave‐one‐well‐out cross‐validation confirms transferability within sampled environments ( R 2 up to 0.85), but leave‐one‐setting‐out reveals systematic extrapolation failure to unsampled depositional environments (basin center R 2 = 0.36; platform R 2 = −2.6). These results indicate that calibrated uncertainty quantification, not point prediction, is the appropriate paradigm for ML‐based source rock assessment and that prediction models must be trained on data spanning the full range of target depositional environments to avoid systematic extrapolation failure.
Gas disasters in tunnels crossing bitumen-bearing sandstones are a distinct geohazard, fundamentally different from coalbed methane risks. The Huangjialiang Tunnel on the Xi'an-Chengdu high-speed railway (Longmenshan foreland and Sichuan Basin) provides an exceptional case study of combined gas-bitumen outbursts. Through an integrated approach combining geological characterisation, real-time drilling monitoring, high-precision geochemical fingerprinting (delta 13C and biomarkers) and process-based logical deduction, this study establishes the geological genesis and engineering disaster mechanisms of such events. Carbon isotope data show that methane (delta 13C1 = -33.8 parts per thousand) and bitumen extracts (delta 13C1 = -34.2 parts per thousand) share a common origin from Lower Cambrian source rocks. Gas occurrence is controlled by structural-lithological coupling: The Longmenshan fault system provided migration pathways; dual-porosity sandstones act as reservoirs; mudstone interbeds serve as local seals; and bitumen-plugged fracture networks create high-pressure 'gas pockets' via Jamin effects, supported by numerical simulations showing a 40% permeability reduction. Excavation triggers a four-stage chain: (1) stress unloading generates tensile microcracks, (2) multiphase fluids ingress, (3) H2S accumulates and corrodes concrete, and (4) safety thresholds are exceeded. The mean H2S concentration of 5.92 vol.% highlights acute toxicity and long-term structural corrosion risks. This study synthesises existing knowledge into an integrated conceptual framework for this under-explored hazard type, offering practical guidance for risk mitigation in similar petrogenic formations worldwide.
The Mesozoic clastic reservoirs in the Tanhai area, Jiyang Depression, show low porosity, low permeability, and strong heterogeneity, making effective reservoir prediction difficult. We integrated core, log, and seismic data to build a four-parameter evaluation model using porosity, permeability, oil saturation, and sedimentary facies. Weights were determined by combining principal component analysis and grey relational analysis. The overall minimum cutoffs for effective reservoirs are porosity greater than 7%, permeability greater than 0.2 & times; 10-3 & micro;m2, and oil saturation greater than 50%, restricted to deltaic and braided-river sandstones and glutenites. Two types of effective reservoirs are identified. Type I reservoirs consist predominantly of glutenites developed in deltaic facies, with primary or fracture-secondary pores; their porosity exceeds 8% and permeability exceeds 0.3 & times; 10-3 & micro;m2. Type II reservoirs consist predominantly of sandstones developed in braided river facies, with secondary-fracture pores; their porosity ranges from 7% to 8% and permeability from 0.2 to 0.3 & times; 10-3 & micro;m2. The spatial distribution model reveals that reservoir distribution is jointly controlled by a fault-unconformity system. Effective reservoirs are concentrated along major fault zones and near unconformities at structural highs. They are continuous within fault blocks but discontinuous between blocks, and their thickness decreases with burial depth. Model validation using independent well-test data yields a prediction accuracy of 85.71%, as shown by a confusion matrix. This study provides an effective methodological workflow for predicting strongly heterogeneous clastic reservoirs.
The precise evaluation of the saturation exponent is essential for dependable hydrocarbon-saturation estimates in oil and gas reservoirs. Conventional laboratory techniques, however, are often slow and expensive. This study carried out controlled experiments on a range of tight sandstone cores and then built several advanced machine-learning (ML) models to predict the saturation exponent directly from nuclear magnetic resonance (NMR) measurements. Porosity, permeability, and NMR T2lm served as the predictive inputs. Outliers were detected using the leverage method, and a sensitivity assessment clarified how strongly each variable influenced the exponent. Model consistency was confirmed using k-fold cross-validation, and among all evaluated methods, AdaBoost achieved the strongest performance, showing the highest predictive accuracy and the lowest errors relative to the experimental data. The analysis also showed that permeability exerted the greatest influence on the saturation exponent, a conclusion that aligned closely with detailed core analyses and laboratory results.
The diagenetic albitization of plagioclase and K-feldspar grains is a transformative process that significantly alters sandstone's detrital composition and formation water chemistry, impacting reservoir quality. In this study, Eocene turbidite sandstones from the offshore Esp & iacute;rito Santo Basin, Brazil, were investigated to elucidate the role of albitization in porosity and permeability. Petrographic, geochemical, and petrophysical analyses revealed that feldspar albitization contributed to reservoir quality by creating intragranular porosity. The degree of albitization is influenced by feldspar composition. Orthoclase is more extensively albitized than microcline. Calcic plagioclase with 40-60 mol% anorthite is more albitized than sodic plagioclase (20-40 mol% An) derived from amphibolitic metamorphic rocks. Albitization was more pronounced in distal turbidite sandstones, owing to their deeper burial history. The process occurred within a temperature range of approximately 60-100 degrees C, as inferred from paragenetic sequences and isotopic data. Although intragranular porosity associated with albitization exhibits a moderate positive correlation with porosity, its impact on permeability remains negligible. These findings advance the understanding of diagenetic albitization and its implications for reservoir quality in albitized clastic systems, offering insights into the controls and conditions of albitization and its broader significance in hydrocarbon exploration.
Reservoir characterization in carbonates has long been a divisive topic between geologists, petrophysicists and reservoir engineers. Permeability prediction in carbonate rocks is usually treated as a petrophysical black box due to the complexity of carbonate reservoirs. This study describes the sedimentology of cores from a well penetrating Miocene carbonate from Central Luconia, offshore Sarawak, Malaysia, and characterizes the petrophysical qualities of the carbonate reservoir using core-derived sedimentological data. The study also constructs an integrated sedimentology-based petrophysical rock type classification scheme for carbonate reservoir characterization. This involves integration of core and petrographic description to provide geological context to routine core analysis (RCA) and mercury injection capillary pressure (MICP) data. The results can be applied in the modelling stage to allow sedimentological and diagenetic information to be considered independently. Eleven facies are identified based primarily on biota distribution and rock fabric. The resulting facies form elements of three facies associations, namely, lagoon, reef and slope. Five rock types (RT1 to RT5) are identified on the basis of the cumulative frequency distribution of calculated flow zone indicators (FZI). High quality reservoirs (RT1, RT2 and RT3) in all three facies associations are dominated by floatstone facies with grainstone or packstone matrix, framestone facies as well as packstone facies. Reservoir properties were found to be widely distributed even within individual facies associations due to several factors, particularly the primary depositional fabric and the presence of secondary processes. The similar reservoir quality observed in coraline floatstone with skeletal algal packstone facies, algal packstone and skeletal packstone facies clearly suggests matrix control on reservoir quality in coarser-grained deposits. The distribution of good reservoir properties is due to the presence of primary porosity and significant secondary porosity in floatstone facies. The significantly lower reservoir quality of skeletal packstone is due to the muddy matrix, which reduced the primary porosity and significant cementation that occurred afterwards.
This study developed a Python-based computer application, RESOILSAT, for estimating petrophysical exponents and residual oil saturation (S-or) in "FAS" Field, Niger Delta. The motivation arose from the need to address the observed lapses in the Archie method for estimating petrophysical exponents and fluid saturation, especially in shaly sand reservoirs. It provides an alternative approach that complements existing fluid saturation models for shaly sands, such as the Simandoux, dual porosity, Waxman-Smits, and Indonesian techniques. The study utilized eight wells ("FAS"-01, "FAS"-02, "FAS"-03, "FAS"-04, "FAS"-05, "FAS"-06, "FAS"-07, and FAS ''-08), which consist of well logs (gamma ray, resistivity, neutron, density, and sonic) and core S-or data from wells ("FAS"-01, "FAS"-02, and "FAS"-03) as input data for the developed application. The application was developed around the conventional (Archie) method and maximum likelihood estimation (MLE) method, which involved the gradient descent (G-D) optimization algorithm to estimate petrophysical exponents and S-or in the delineated reservoirs (Res-A and Res-B). Results show that, with the conventional Archie method, tortuosity (a) and cementation (m) are constant, 0.21 and 2.15, respectively, in "FAS"-01 to "FAS"-08, whereas the saturation exponent (n) varied from 0.40 (Res-B, "FAS"-05) to 8.58 (Res-B, "FAS"-07), and S-or varied from 0.18 (Res-B, "FAS"-05) to 0.97 (Res-A, "FAS"-06). With the Indonesian and Simandoux models, S-or varied from 0.88 ("FAS"-05) to 0.94 ("FAS"-06) and 0.91 ("FAS"-03) to 0.98 ("FAS"-04, "FAS"-05, and "FAS"-08) respectively. With the MLE using the G-D algorithm, a varied from 0.37 (Res-A, "FAS"-04) to 2.80 (Res-A, "FAS"-03), m varied from 0.05 (Res-B, "FAS"-01) to 4.11 (Res-A, "FAS"-04), whereas n varied from 0.66 (Res-B, "FAS"-05) to 8.59 (Res-A, "FAS"-05), and S-or varied from 0.11 (Res-B, "FAS"-05) to 0.95 (Res-A, "FAS"-03). The percentage deviation of the computed S-or relative to the core data varied from 1% to 7% in MLE and 3% to 64% for conventional. t-Test of the computed S-or relative to the core data ranged from 0.44 to 10.10 for MLE and 3.85 to 15.27 for conventional method. The relatively low percentage deviation of the MLE method relative to the core-derived S-or lends credence to its higher reliability in computing S-or in the study area.
The extreme microstructural heterogeneity of tight sandstone reservoirs poses a significant challenge for accurately predicting fluid flow and optimizing hydrocarbon recovery, as conventional petrophysical models often fail to capture complex, multi-scale pore architectures. To address this, this study investigates the diagenetic evolution and reservoir characteristics of the Upper Triassic Chang 6 tight sandstone in the Panlong area, Ordos Basin. By integrating fluid inclusion micro-thermometry, scanning electron microscopy (SEM), and multi-scale fractal geometry (utilizing the Frenkel-Halsey-Hill [FHH] model and high-pressure mercury injection), we systematically quantify how specific diagenetic processes dictate pore geometry. Petrographic analysis reveals the reservoir is predominantly composed of mature arkosic sandstone, where primary porosity (ranging from 0.4% to 17.8%) has been severely diminished by intense mechanical compaction and pervasive authigenic cementation, including calcite, chlorite, and illite. The reservoir pore system exhibits highly heterogeneous, bimodal fractal behavior. Although nanoscale pores (<30 nm) maintain relative structural uniformity, fluid-governing meso- and macropores demonstrate extreme tortuosity strictly driven by diagenetic facies. Specifically, compaction and carbonate cementation exponentially increase fractal complexity and capillary resistance, whereas feldspar dissolution zones yield secondary porosity networks with reduced structural tortuosity. Fluid inclusion data indicate that primary liquid hydrocarbon charging occurred between 100 degrees C and 170 degrees C, coinciding with early-to-middle diagenetic cementation. Ultimately, these findings demonstrate that quantitative multi-scale fractal evaluation is essential for targeting secondary dissolution sweet spots, providing a robust theoretical framework for enhanced exploration strategies in tight continental basins.
Shallow shale gas has the potential to become an important alternative to conventional oil and gas because of its relatively low development cost, limited capital requirement, and rapid economic return. Although commercial success has been widely reported in medium- to high-maturity shale reservoirs, breakthroughs in low-maturity shale reservoirs remain limited. The organic-rich shale of the first member of the Qingshankou Formation (Qing 1 Member) in the Wangfu Depression, Southeastern Uplift, Songliao Basin, is characterized by shallow burial and low thermal maturity. However, gas-logging anomalies identified in several wells indicate considerable exploration potential. The results show that these shales followed a three-stage relay gas-generation model involving early biogenic gas, middle-stage low-maturity thermogenic gas, and late-stage crude-oil biodegradation gas. The present-day shale gas is generally characterized predominantly by low-maturity thermogenic gas, with signatures of biogenic gas and gases derived from crude-oil biodegradation also present to some extent. Organic matter abundance (TOC and oil saturation index [OSI]) and maturity jointly control the capacity of the shale to generate thermogenic gas and subsequent biogenic gas. Shale gas mainly occurs in free and oil-dissolved states and is hosted in intergranular pores, intercrystalline pores, and minor intragranular dissolution pores. During tectonic uplift, the adsorption capacity of the shale first decreases and then increases, whereas the free-gas content continuously decreases. This differential evolution promotes the transformation of free gas into adsorbed gas, which favors the long-term preservation of shale gas.
Organic matter pores serve as the primary storage space for shale oil and gas, and their development degree is controlled by the hydrocarbon generation capacity of organic matter. However, the influence of mineral matrix on hydrocarbon generation capacity has not been fully investigated. In this study, the Permian Gufeng Formation shale (high carbonate mineral content) and the Silurian Longmaxi Formation shale (high clay mineral content) from the Sichuan Basin were selected. By integrating FE-SEM, x-ray photoelectron spectroscopy (XPS), 13C nuclear magnetic resonance (13C NMR), Fourier transform infrared spectroscopy (FT-IR), elemental analysis, kerogen molecular modeling, and ReaxFF MD simulations, we systematically revealed how the mineral matrix influences the pyrolysis pathway of organic matter and the development of organic pores. SEM observations show that organic matter in contact with calcite appears dense and nonporous, whereas the development degree of organic pores significantly increases with higher clay mineral content or when organic matter is in contact with clay minerals. We constructed molecular models based on experimental data of the Gufeng Formation kerogen and performed ReaxFF MD simulations of kerogen pyrolysis from 300 to 3000 K in three environments: pure, montmorillonite interlayer, and calcite. The simulation results show that in the calcite environment, the Lewis acidity of Ca2+ accelerates bridging bond cleavage in the early stage of pyrolysis; however, in the middle-late stage, Ca2+ forms stable complexes with oxygen-containing functional groups and abstracts organic hydrogen to form H2O, leading to a deficiency of free hydrogen species in the system. This inhibits the generation of gaseous hydrocarbons (C1-C4) and hinders pore development. In the montmorillonite environment, the synergistic catalysis of Br & oslash;nsted and Lewis acid sites promotes C-C/C-H bond breaking, ring-opening reactions, and decarboxylation/hydrocracking. This increases the yields of gaseous hydrocarbons (C1-C4), light oil (C5-C13), and heavy oil (C14-C40), thereby providing a sufficient gas source for organic pore development. This study elucidates the intrinsic mechanism by which mineral matrix influences organic pore development through controlling hydrocarbon generation pathways and provides a micro-scale theoretical basis for the evaluation of carbonate-rich shale reservoirs.
