Search Results (230)

Conglomerate reservoirs present significant technical challenges during drilling operations due to their complex mineral composition and heterogeneous characteristics, yet the quantitative relationships between mineral composition and microscopic mechanical behavior remain poorly understood. To elucidate the variation patterns of conglomerate micromechanical properties and their mineralogical control mechanisms, this study develops a novel multi-scale characterization methodology. This approach uniquely couples nanoindentation technology, micro-zone X-ray diffraction analysis, and machine learning algorithms to systematically investigate micromechanical properties of conglomerate samples from different regions. Hierarchical clustering algorithms successfully classified conglomerate micro-regions into three lithofacies categories with distinct mechanical differences: hard (elastic modulus: 81.90 GPa, hardness: 7.83 GPa), medium-hard (elastic modulus: 54.97 GPa, hardness: 3.87 GPa), and soft lithofacies (elastic modulus: 25.21 GPa, hardness: 1.15 GPa). Correlation analysis reveals that quartz (SiO₂) content shows significant positive correlation with elastic modulus (r = 0.52) and hardness (r = 0.51), while clay minerals (r = −0.37) and plagioclase content (r = −0.48) exhibit negative correlations with elastic modulus. Mineral phase spatial distribution patterns control the heterogeneous characteristics of conglomerate micromechanical properties. Additionally, a random forest regression model successfully predicts mineral content based on hardness and elastic modulus measurements with high accuracy. These findings bridge the gap between microscopic mineral properties and macroscopic drilling performance, enabling real-time formation strength assessment and providing scientific foundation for optimizing drilling strategies in heterogeneous conglomerate formations. Full article

We have developed a generalisable machine learning framework for reservoir quality prediction in deeply buried clastic systems. Applied to the Lower Jurassic deltaic sandstones of the Tilje Formation (Halten Terrace, North Sea), the approach integrates sedimentological facies modelling with mineralogical and petrophysical prediction in a single workflow. Using supervised Extreme Gradient Boosting (XGBoost) models, we classify reservoir facies, predict permeability directly from standard wireline log parameters and estimate the abundance of porosity-preserving grain coating chlorite (gamma ray, neutron porosity, caliper, photoelectric effect, bulk density, compressional and shear sonic, and deep resistivity). Model development and evaluation employed stratified K-fold cross-validation to preserve facies proportions and mineralogical variability across folds, supporting robust performance assessment and testing generalisability across a geologically heterogeneous dataset. Core description, point count petrography, and core plug analyses were used for ground truthing. The models distinguish chlorite-associated facies with up to 80% accuracy and estimate permeability with a mean absolute error of 0.782 log(mD), improving substantially on conventional regression-based approaches. The models also enable prediction, for the first time using wireline logs, grain-coating chlorite abundance with a mean absolute error of 1.79% (range 0–16%). The framework takes advantage of diagnostic petrophysical responses associated with chlorite and high porosity, yielding geologically consistent and interpretable results. It addresses persistent challenges in characterising thinly bedded, heterogeneous intervals beyond the resolution of traditional methods and is transferable to other clastic reservoirs, including those considered for carbon storage and geothermal applications. The workflow supports cost-effective, high-confidence subsurface characterisation and contributes a flexible methodology for future work at the interface of geoscience and machine learning. Full article

The Lower Congo Basin (LCB) and the Niger Delta Basin (NDB), two end-member deep-water systems along the West African passive margin, exhibit contrasting sedimentary architectures despite shared geodynamic settings. The research comprehensively utilizes seismic reflection structure, root mean square amplitude slices, drilling lithology, changes in logging curves, and previous research achievements to elucidate the controlling mechanisms behind these differences. Key findings include: (1) Stark depositional contrast: Since the Eocene, the LCB developed retrogradational narrow-shelf systems dominated by erosional channels and terminal lobes, whereas the NDB formed progradational broad-shelf complexes with fan lobes and delta-fed turbidites. (2) Primary controls: Diapir-driven topographic features and basement uplift govern architectural variability, whereas shelf-slope break configuration and oceanic relief constitute subordinate controls. (3) Novel mechanism: First quantification of how diapir-induced seafloor relief redirects sediment pathways and amplifies facies heterogeneity. These insights establish a tectono-sedimentary framework for predicting deep-water reservoirs in diapir-affected passive margins, refine the conventional “source-to-sink” model by emphasizing salt-geomorphic features coupling as the primary driver. By analyzing the differences in lithofacies assemblages and sedimentary configurations among the above-mentioned different basins, this study can provide beneficial insights for the research on related deep-water turbidity current systems and also offer guidance for deep-water oil and gas exploration and development in the West African region and other similar areas. Full article

The description and assessment of braided river architecture are usually limited by the paucity of real geological datasets from field observations; due to the complexity and diversity of rivers, traditional evaluation models are difficult to apply to braided river systems in different climatic and tectonic settings. This study aims to establish an architectural model suitable for the study area setting by introducing a hierarchical analysis approach through well-exposed three-dimensional outcrops along the Second Songhua River. A micro–macro four-level hierarchical framework is adopted to obtain a detailed anatomy of sedimentary outcrops: lithofacies, elements, element associations, and archetypes. Fourteen lithofacies are identified: three conglomerates, seven sandstones, and four mudstones. Five elements provide the basic components of the river system framework: fluvial channel, laterally accreting bar, downstream accreting bar, abandoned channel, and floodplain. Four combinations of adjacent elements are determined: fluvial channel and downstream accreting bar, fluvial channel and laterally accreting bar, erosionally based fluvial channel and laterally accreting bar, and abandoned channel and floodplain. Considering the sedimentary evolution process, the braided river prototype, which is an element-based channel filling unit, is established by documenting three contact combinations between different elements and six types of fine-grained deposits’ preservation positions in the elements. Empirical relationships are developed among the bankfull channel depth, mean bankfull channel depth, and bankfull channel width. For the braided river systems, the establishment of the model promotes understanding of the architecture and evolution, and the application of the hierarchical analysis approach provides a basis for outcrop, underground reservoir, and tank experiments. Full article

The lacustrine to deltaic depositional systems of the Upper Jurassic Qigu Formation in the Yongjin area constitute a significant petroleum reservoir in the central Junggar Basin, China. Based on core observations, petrology analyses, paleoenvironment indicators and modern sedimentary analyses, sequence stratigraphy, lithofacies associations, sedimentary environment, evolution, and models were investigated. The Qigu Formation can be divided into a third-order sequence consisting of a lowstand systems tract (LST) and a transgressive systems tract (TST), which is further subdivided into six fourth-order sequences. Thirteen lithofacies and five lithofacies associations were identified, corresponding to shallow-water delta-front deposits. The paleoenvironment of the Qigu Formation is generally characterized by an arid freshwater environment, with a dysoxic to oxic environment. During the LST depositional period (SQ1–SQ3), the water depth was relatively shallow with abundant sediment supply, resulting in a widespread distribution of channel and mouth bar deposits. During the TST depositional period (SQ4–SQ6), the rapid rise in base level, combined with reduced sediment supply, resulted in swift delta retrogradation and widespread lacustrine sedimentation. Combined with modern sedimentary analysis, the shallow-water delta in the study area primarily comprises a composite system of single main channels and distributary channel-mouth bar complexes. The channel-bar complex eventually forms radially distributed bar assemblages with lateral incision and stacking. The distributary channel could incise a mouth bar deeply or shallowly, typically forming architectural patterns of going over or in the mouth bar. Reservoir test data suggest that the mouth bar sandstones are favorable targets for lithological reservoir exploration in shallow-water deltas. Full article

In the absence of appropriate tools and a knowledge base for exploring high-grade metamorphic terrains, felsic gneiss complexes at granulite facies have long been considered barren and have remained undermapped and understudied. This was the case of the Bondy gneiss complex in the southwestern Grenville Province of Canada which consists of 1.39–1.35 Ga volcanic and plutonic rocks metamorphosed under granulite facies conditions at 1.19 Ga. Iron oxide–apatite and Cu-Ag-Au mineral occurrences occur among gneisses rich in biotite, cordierite, garnet, K-feldspar, orthopyroxene and/or sillimanite-rich gneisses, plagioclase-cordierite-orthopyroxene white gneisses, magnetite-garnet-rich gneisses, garnetites, hyperaluminous sillimanite-pyrite-quartz gneisses, phlogopite-sillimanite gneisses, and tourmalinites. Petrological and geochemical studies indicate that the precursors of these gneisses are altered volcanic and volcaniclastic rocks with attributes of pre-metamorphic Na, Ca-Fe, K-Fe, K, chloritic, argillic, phyllic, advanced argillic and skarn alteration. The nature of these hydrothermal rocks and the ore deposit model that best represents them are further investigated herein through lithogeochemistry. The lithofacies mineralized in Cu (±Au, Ag, Zn) are distinguished by the presence of garnet, magnetite and zircon, and exhibit pronounced enrichment in Fe, Mg, HREE and Zr relative to the least-altered rocks. In discrimination diagrams, the metamorphosed mineral system is demonstrated to exhibit the diagnostic attributes of, and is interpreted as, a metasomatic iron and alkali-calcic (MIAC) mineral system with iron oxide–apatite (IOA) and iron oxide copper–gold (IOCG) mineralization that evolves toward an epithermal cap. This contribution demonstrates that alteration facies diagnostic of MIAC systems and their IOCG and IOA mineralization remain diagnostic even after high-grade metamorphism. Exploration strategies can thus use the lithogeochemical footprint and the distribution and types of alteration facies observed as pathfinders for the facies-specific deposit types of MIAC systems. Full article

Lithology identification is one of the most important research areas in petroleum engineering, including reservoir characterization, formation evaluation, and reservoir modeling. Due to the complex structural environment, diverse lithofacies types, and differences in logging data and core data recording standards, there is significant overlap in the logging responses between different lithologies in the second member of the Lucaogou Formation in the Santanghu Basin. Machine learning methods have demonstrated powerful nonlinear capabilities that have a strong advantage in addressing complex nonlinear relationships between data. In this paper, based on felsic content, the lithologies in the study area are classified into four categories from high to low: tuff, dolomitic tuff, tuffaceous dolomite, and dolomite. We also study select logging attributes that are sensitive to lithology, such as natural gamma, acoustic travel time, neutron, and compensated density. Using machine learning methods, XGBoost, random forest, and support vector regression were selected to conduct lithology identification and favorable reservoir prediction in the study. The prediction results show that when trained with 80% of the predictors, the prediction performance of all three models has improved to varying degrees. Among them, Random Forest performed best in predicting felsic content, with an MAE of 0.11, an MSE of 0.020, an RMSE of 0.14, and a R² of 0.43. XGBoost ranked second, with an MAE of 0.12, an MSE of 0.022, an RMSE of 0.15, and an R² of 0.42. SVR performed the poorest. By comparing the actual core data with the predicted data, it was found that the results are relatively close to the XRD results, indicating that the prediction accuracy is high. Full article

Based on drilling, core and seismic data, combined with the regional tectonic sedimentary evolution background, the sedimentary environment of the Triassic carbonate rocks in the Mangeshlak Basin was studied. A sedimentary facies model of this set of carbonate rocks was established. Research has shown that the Mangeshlak Basin underwent a complete large-scale marine transgression–regression sedimentary evolution process during the Triassic. During the early to middle Triassic, seawater gradually invaded the northwest region of the basin from northwest to southeast and gradually regressed in the late Middle Triassic. In the lower part of the Triassic carbonate rocks, the primary components are developed granular limestone or dolomite with oolitic structures, interspersed with a small amount of thin mudstone, which is a good reservoir; the upper part of the Triassic is mainly composed of sedimentary mudstone and mudstone, which can form good sealings. The hill-shaped reflections of the platform edge facies, along with the high-frequency, strong-amplitude, and moderately continuous reflections within the restricted platform interior, are clearly visible on the seismic profile. These features are consistent with the sedimentary environment and lithofacies characteristics revealed by drilling data along the profile. Drilling and seismic data revealed that the sedimentary environment of the early and middle Triassic in the basin is mainly composed of shallow water platform edges and restricted platforms, as well as carbonate rock slopes and open non-marine shelves in deep water areas. A sedimentary facies model of the Triassic carbonate rock segment in the basin was established, comprising restricted platforms, platform edges, carbonate rock slopes, and non-marine shelves. Unlike the modified Wilson marginal carbonate rock platform model, the carbonate rock platform edge in the Mangeshlak Basin does not develop reef facies. Instead, it is mainly composed of oolitic beach (dam) sediments, making it the most favorable sedimentary facies zone for the Triassic reservoir development in the basin. Full article

Amid increasing interest in enhanced oil recovery and carbon geological sequestration programs, improved static reservoir lithofacies models are emerging as a requirement for well-guided project management. Building reservoir models can leverage seismic attribute clustering for seismic facies mapping. One challenge is that machine learning (ML) seismic facies mapping is prone to a wide range of equally possible outcomes when traditional unsupervised ML classification is used. There is a need to constrain ML seismic facies outcomes to limit the predicted seismic facies to those that meet the requirements of geological plausibility for a given depositional setting. To this end, this study utilizes an unsupervised comparative hierarchical and K-means ML classification of the whole 3D seismic data spectrum and a suite of spectral bands to overcome the cluster “facies” number uncertainty in ML data partition algorithms. This comparative ML, which was leveraged with seismic resolution data preconditioning, predicted geologically plausible seismic facies, i.e., seismic facies with spatial continuity, consistent morphology across seismic bands, and two ML algorithms. Furthermore, the variation of seismic facies classes was validated against observed lithofacies at well locations for the Mississippian carbonates of Kansas. The study provides a benchmark for both unsupervised ML seismic facies clustering and an understanding of seismic facies implications for reservoir/saline-aquifer aspects in building reliable static reservoir models. Three-dimensional seismic reflection P-wave data and a suite of well logs and drilling reports constitute the data for predicting seismic facies based on seismic attribute input to hierarchical analysis and K-means clustering models. The results of seismic facies, six facies clusters, are analyzed in integration with the target-interval mineralogy and reservoir porosity. The study unravels the nature of the seismic (litho) facies interplay with porosity and sheds light on interpreting unsupervised machine learning facies in tandem with both reservoir porosity and estimated (Umaa-RHOmaa) mineralogy. Full article

The lithofacies and pore structural characteristics of shale reservoirs directly affect the exploration and development of shale gas. To clarify the exploration and development potential of the Lower Cambrian Shuijingtuo Formation (SJT) shale in the Middle Yangtze region, China, this study employs integrated experimental approaches, including optical and scanning electron microscopy (SEM) observations, X-ray diffraction (XRD) mineralogical analysis, and low-pressure gas (N₂/CO₂) adsorption, to classify mudstone lithofacies within the SJT and elucidate pore structural characteristics and dominant geological control across different lithofacies. The research results show that (1) Six main types of shale lithofacies are found in the STJ, including low-TOC massive calcareous mudstone (LMCM), low-TOC laminated mixed mudstone (LLMM), medium-TOC massive mixed mudstone (MMMM), high-TOC massive mixed mudstone (HMMM), high-TOC laminated siliceous mudstone (HLSM), and laminated argillaceous mudstone (LAM). (2) The pore types of SJT mudstone primarily include organic pores, intragranular clay mineral pores, and microfractures. The pore structure of mudstone is mainly controlled by clay mineral content and TOC content. However, the controlling factors of pore structure vary among different mudstone lithofacies. LMCM and LLMM are dominated by intragranular clay mineral pores, with their pore structures mainly controlled by clay mineral content. The pore types of HLMM and HLSM are organic pores, with pore structures predominantly controlled by TOC content. (3) The SJT mudstone gas reservoir exhibits diverse types, including HLSM, LAM, and LLMM. HLSM is characterized by the highest brittleness index and elevated pore volume (PV) and it can be considered the optimum lithofacies in the study area. Additionally, LLMM has the highest PV and relatively high brittleness index, positioning it as another significant reservoir target in the study area. Therefore, the Lower Cambrian shale gas reservoirs in the Middle Yangtze region exhibit diverse reservoir types. These research findings provide a scientific basis for the next phase of shale gas exploration planning in the Lower Cambrian. Full article

Sedimentary facies identification constitutes a cornerstone of reservoir engineering. Traditional facies interpretation methods, reliant on manual log-response parameter analysis, are constrained by interpreter subjectivity, reservoir heterogeneity, and inefficiencies in resolving thin interbedded sequences and concealed fluvial sand bodies—issues marked by high interpretive ambiguity, prolonged cycles, and elevated costs. This study focuses on the Lower Cretaceous Yaojia Formation Member 1 (K2y1) in the satellite oilfield of the Songliao Basin, integrating sequence stratigraphy into a machine learning framework to propose an innovative convolutional neural network (CNN)-based facies recognition method using log-curve image groups by graphically transforming five log curves and establishing a CNN model that correlates log responses with microfacies. Results demonstrate the model’s capability to identify six microfacies types (e.g., subaqueous distributary channels, estuary bars, sheet sands) with 83% accuracy, significantly surpassing conventional log facies analysis. This breakthrough in interpreting complex heterogeneous reservoir lithofacies establishes a novel technical avenue for intelligent exploration of subtle hydrocarbon reservoirs. Full article

We conducted ultrasonic (1-MHz) laboratory measurements on 210 samples from the Vaca Muerta Formation (Neuquén Basin, Argentina) to determine the factors influencing acoustic velocities in siliciclastic–carbonate mudstone. We quantitatively assessed the calcium carbonate and total organic carbon (TOC) content and qualitatively identified the quartz and clay mineralogy. For brine-saturated samples, P-wave velocities ranged from 2826 to 6816 m/s, S-wave velocities ranged from 1474 to 3643 m/s, and porosity values ranged from 0.01 to 19.4%. Carbonate content percentages, found to be critically important, vary widely from 0.08 to 98.0%, while TOC ranged from 0 to 5.3%. Velocity was primarily controlled by carbonate content and, to a lesser extent, by the non-carbonate mineralogy of the rock (e.g., quartz, clay minerals). TOC content had little effect on the acoustic properties. Due to the low porosity of most samples, mineral composition had a stronger influence on velocity than porosity or pore geometry. The V_p/V_s ratio of dry samples ranged from 1.38 to 1.97 and decreased as porosity increased. In saturated samples, the V_p/V_s ratio ranged from 1.46 to 2.06 and appeared independent of porosity. A clear distinction between carbonate and mixed lithofacies under both saturated and dry conditions was observed in all samples. Full article

The Placerias quarry is a dicynodont-dominated bonebed in Upper Triassic Chinle Group strata near St. Johns in east-central Arizona, USA. Though long identified as being in strata of the lower Chinle Group, recently published numerical ages apparently indicate a stratigraphically much higher (younger) position in the Chinle section for the Placerias quarry. Nevertheless, recent analysis of outcrop and subsurface (hydrologic) data in the vicinity of the Placerias quarry confirms its stratigraphic position very low in the Chinle Group section, close to the base of the Bluewater Creek Formation. A regional Upper Triassic lithostratigraphy has been established across east-central Arizona and west-central New Mexico by nearly a century of stratigraphic studies and geologic mapping by diverse workers, and is supported by biostratigraphy; in this lithostratigraphy the Placerias quarry is near the Chinle Group base. However, U/Pb ages on zircons from Upper Triassic strata in eastern Arizona/western New Mexico have been used to reorganize this lithostratigraphy to indicate intertonguing and dramatic lithofacies changes over relatively short lateral distances. But, if the well-established lithostratigraphy is followed, the U/Pb ages are problematic, particularly where younger ages (such as at the Placerias quarry) are stratigraphically below older ages. A handful of numerical ages should not be used to over-rule well-established understanding of lithostratigraphy and biostratigraphy, unless the lithostratigraphy and biostratigraphy need to be modified based on stratigraphic data. Numerical ages need to be used judiciously and evaluated critically with regard to established lithostratigraphy, biostratigraphy and other age constraints. Full article

In recent years, significant exploration successes and research progress in volcanic hydrocarbon reservoirs across China’s offshore basins have highlighted their importance as key targets for deep hydrocarbon exploration. In the Shimentan Formation of the Xihu Sag, East China Sea Shelf Basin (ECSSB), low-yield gas flows have been encountered through exploratory drilling; however, no major reservoir breakthroughs have yet been achieved. Assessing the large-scale reservoir potential of volcanic sequences in the Shimentan Formation is thus critical for guiding future exploration strategies. Based on previous exploration studies of volcanic reservoirs in other Chinese basins, this study systematically evaluates the hydrocarbon potential of these volcanic units by microscopic thin section identification, major element analysis, integrates drilling data with seismic interpretation techniques—such as coherence cube slicing for identifying volcanic conduits, dip angle analysis for classifying volcanic edifices, and waveform classification for delineating volcanic lithofacies. The main findings are as follows: (1) The Shimentan Formation is primarily composed of intermediate to acidic pyroclastic rocks and lava flows. Volcanic facies are divided into three facies, four subfacies, and six microfacies. Volcanic edifices are categorized into four types: stratified, pseudostratified, pseudostratified-massive, and massive. (2) Extensive pseudostratified volcanic edifices are developed in the Hangzhou Slope Zone, where simple and compound lava flows of effusive facies are widely distributed. (3) Comparative analysis with prolific volcanic reservoirs in the Songliao and Bohai Bay basins indicates that productive reservoirs are typically associated with simple or compound lava flows within pseudostratified edifices. Furthermore, widespread Late Cretaceous rhyolites in adjacent areas of the study region suggest promising potential for rhyolitic reservoir development in the Hangzhou Slope Zone. These results provide a robust geological foundation for Mesozoic volcanic reservoir exploration in the Xihu Sag and offer a methodological framework for evaluating reservoir potential in underexplored volcanic regions. Full article

The production of gas and condensate from liquid-rich shale reservoirs, particularly within heterogeneous lacustrine systems, remains a critical challenge in unconventional hydrocarbon exploration due to intricate multiphase hydrocarbon partitioning, including gases (C₁–C₂), volatile liquids (C₃–C₇), and heavier liquids (C₇₊). This study investigates a 120-meter-thick interval dominated by lacustrine deposits from the Lower Cretaceous Shahezi Formation (K₁sh) in the Songliao Basin. This interval, characterized by high clay mineral content and silicate–pyrite laminations, was examined to identify the factors controlling hybrid shale gas condensate systems. We proposed the Hybrid Shale Condensate Index (HSCI), defined as the molar ratios of (C₁–C₇)/C₇₊, to categorize fluid phases and address shortcomings in traditional GOR/API ratios. Over 1000 samples were treated by geochemical pyrolysis logging, X-ray fluorescence (XRF) spectrum element logging, SEM-based automated mineralogy, and in situ gas desorption, revealing four primary controls: (1) Thermal maturity thresholds. Mature to highly mature shales exhibit peak condensate production and the highest total gas content (TGC), with maximum gaseous and liquid hydrocarbons at T_max = 490 °C. (2) Lithofacies assemblage. Argillaceous shales rich in mixed carbonate and clay minerals exhibit an intergranular porosity of 4.8 ± 1.2% and store 83 ± 7% of gas in intercrystalline pore spaces. (3) Paleoenvironmental settings. Conditions such as humid climate, saline water geochemistry, anoxic bottom waters, and significant input of volcanic materials promoted organic carbon accumulation (TOC reaching up to 5.2 wt%) and the preservation of organic-rich lamination. (4) Laminae and fracture systems. Silicate laminae account for 78% of total pore space, and pyrite laminations form interconnected pore networks conducive to gas storage. These findings delineate the “sweet spots” for unconventional hydrocarbon reservoirs, thereby enhancing exploration for gas condensate in lacustrine shale systems. Full article