Reservoir Lithofacies Research Articles

Improving lithofacies prediction in lacustrine shale by combining deep learning and well log curve morphology in Sanzhao Sag, Songliao Basin, China

The accurate identification of shale lithofacies is crucial for characterizing the hydrocarbon potential of lacustrine shale oil reservoirs. Petrophysical logging, serving as an effective tool for acquiring subsurface lithofacies information, provides a convenient and reliable lithofacies identification solution. Deep learning technology, capable of adapting to the nonlinearity and non-stationarity inherent in geological statistics, exhibits unique advantages in conventional reservoir lithofacies prediction. However, the lithofacies of lacustrine shale formations undergo rapid spatial and temporal changes, rendering lithofacies prediction more complex compared to conventional reservoirs. In this study, the lower Qingshankou member in the Sanzhao Sag was selected as the research target, and the Deep Residual Shrinkage Network (DRSN), known for its ability to handle complex nonlinear relationships and mitigate the effects of noisy data through residual connections and shrinkage mechanisms, was employed as a deep learning framework for predicting lithofacies in lacustrine shale formations for the first time. Well logging data, including natural gamma ray (GR), acoustic (AC), deep investigate double lateral resistivity log (RD), shallow investigate double lateral resistivity log (RS), and corrected compensated neutron log (CNC), were used as input features for the model. The results indicate that the DRSN model achieves an accuracy of 76.3% in predicting lithofacies in lacustrine shale formations. However, the DRSN model still exhibits shortcomings in capturing lithofacies change information. To enhance the model's ability to identify lithofacies change interfaces, this study further explicitly introduces Well Logging Curve Morphological Features (WLCM) as additional features and establishes a recognition method combining DRSN with WLCM. The combined DRSN-WLCM model was validated using a separate test dataset, demonstrating an improved accuracy of 85.5%, using the five well logging attributes and the derivative of the AC as inputs. Furthermore, the study reveals the lithofacies spatial distribution characteristics of the lower Qingshankou member in the Sanzhao Sag. This method can be widely applied to lithofacies delineation in lacustrine shale formations and similar stratigraphic units.

Integrating 3D subsurface imaging, seismic attributes, and wireline logging analyses: Implications for a high resolution detection of deep-rooted gas escape features, eastern offshore Nile Delta, Egypt

The eastern part of the offshore Nile Delta is one of Egypt's most productive gas provinces. This study utilizes well logs dataset and 2D seismic from Port Fouad marine (PFM) Field to investigate reservoir lithofacies, rock-types as well as the associated gas chimneys, pockmarks, bright anomalies of escaping fluids in the seafloor, and high-faulted zones caused by the chimney's developments. First, structural smoothing and median filters are used to improve the continuity of the stratigraphic horizons while preserving various geological structures, in order to pick all formation tops and structural faults. Afterwards, variance and energy attributes were computed and extracted for amplitude enhancement of various geobodies such as faults, gas chimneys, and bright reflectors. After that, petrophysical analyses revealed that the Upper Miocene Wakar Formation hosts scattered, thin gas pay zones (<10m) with low hydrocarbon saturation values (average Shc = 27.9%), high water saturation (average Sw > 70%), and high shale volume (average Vsh > 30%). These poor reservoir characteristics prevented accumulation of thick gas zones, thereby several gas escape features (e. vertical chimneys) are observed in the region. Finally, the estimated petrophysical parameter logs and the structure maps derived from seismic data are crucial components of the built 3D structural model. This model reveals the presence of four major normal faults (F1, F2, F3, and F4) oriented NW-SE and WNW-ESE, creating grabens and half grabens. These major faults (F1, F2, F3, and F4) are directly related to the inverted tectonics during the Cretaceous-Paleogene compression (Syrian Arc phase). A link between these structural patterns and gas escape zones is proposed, and the absence of good reservoir facies likely promoted an active phase of gas seepage in the study region. In addition, such gas escape chimneys can create catastrophic events on the seafloor and are considered direct evidence for the presence of gas generation and failure in accumulation.

Geologic characterization of the Lower Cretaceous upper Travis Peak and Pettet Formations in the Wright Mountain field, Smith County, East Texas basin: Potential reservoir analog for other Travis Peak and Pettet strata

The Wright Mountain field in Smith County, Texas, is a tight carbonate reservoir that produces from the Lower Cretaceous Pettet Formation. The Pettet Formation is dominantly made up of cyclic shallow-water shelfal carbonates associated with skeletal oolitic shoal complexes. These shoals are widespread hydrocarbon-reservoir targets, have poor to fair matrix reservoir quality, and are commonly sealed by transgressive argillaceous lime mudstones and wackestones. Complex regional paleotopography has significant impact on the regional distribution of these shoal bodies and overall depositional patterns. This investigation, therefore, aims to characterize the Pettet Formation and underlying fluviodeltaic Travis Peak Formation in the Wright Mountain field, located on the western flank of the Strickland High near the northeastern margin of the East Texas Basin, to assess reservoir parameters and to compare depositional patterns to existing shoal-complex studies centered on the top of the paleohigh in Rusk County. The Pettet section in Smith County consists of three fourth-order sequences (Pettet A–C), each of which shoals upwards into a shallow-water, high-energy environment near or within shoal complexes. The best reservoir lithofacies are the mixed skeletal-ooid and skeletal grainstones, as observed elsewhere in the Pettet Formation in Rusk County. However, texture and biota vary in the Wright Mountain field area as compared to the Rusk County area as there is abundant evidence of reworking and resedimentation in the Wright Mountain field area, suggesting a different depositional setting perhaps related to off-shoal deposition or greater depositional dip. Therefore, this field characterization study is important for constructing a regional depositional model and can be applied as an analog for equivalent systems with similar lithofacies and pore networks elsewhere in the northern Gulf of Mexico.

Heterogeneous Domain Adaptation Framework for Logging Lithofacies Identification

Summary Reservoir lithofacies type is an important indicator of reservoir quality and oiliness, and understanding lithofacies type can help geologists and engineers make informed decisions about exploration and development activities. The use of well-log data to establish machine learning models for lithofacies identification has gained popularity; however, the assumption that data are independent identical distribution followed by these models is often unrealistic. Additionally, there is a possible incompatibility between the training and test data in terms of feature space dimensions. We propose the heterogeneous domain adaptation framework for logging lithofacies identification (HDAFLI) to address these problems. The framework comprises three main contributions: (i) The denoising autoencoder feature mapping (DAFM) module is adopted to resolve the incompatibility issue in feature space between training and test data. The connection between training and test data can be effectively established to improve the performance and generalization ability. (ii) The transferability and discriminative joint probability distribution adaptive (TDJPDA) module addresses the issue of data distribution differences. It improves the transferability of training and test data by minimizing the maximum mean difference (MMD) of the joint probabilities of the source and target domains and enhances their discriminative ability by maximizing the joint probability MMD of different lithofacies categories. (iii) Bayesian optimization is used to optimize hyperparameters in the light gradient boosting machine (LightGBM) model for high computational efficiency in determining the best accuracy. We selected well-logging data from eight wells in the Pearl River Mouth Basin of the South China Sea to design four tasks and compared HDAFLI with various baseline machine learning algorithms and baseline domain adaptive algorithms. The results show that HDAFLI has the highest average accuracy among the four tasks. It is 19.76% and 8.94% higher than the best-performing baseline machine learning algorithm and baseline domain adaptive method among the comparison algorithms, respectively. For HDAFLI, we also conducted ablation experiments, time cost and convergence performance analysis, parameter sensitivity experiments, and feature visualization experiments. The results of ablation experiments show that the three modules of HDAFLI all play an active role, working together to achieve the best results. In addition, HDAFLI has a reasonable time cost, can become stable after several iterations, and has good convergence performance. The results of parameter sensitivity experiments confirm that the accuracy of HDAFLI does not change significantly with changes in hyperparameters, which is robust. The results of feature visualization experiments show that the data of the training set and the test set are concentrated together to a certain extent, which indicates that HDAFLI has completed the task of data distribution alignment very well. The findings of this study can help for a better understanding of how to address the challenge of reservoir lithofacies identification through a heterogeneous domain adaptation framework. By solving the problem of feature space incompatibility and data distribution difference between training data and test data, the application of HDAFLI provides geologists and engineers with more accurate lithofacies classification tools. This study has practical application value for reservoir quality assessment, oiliness prediction, and exploration and development decision-making.

Identification of challenging gas-bearing reservoir based on machine learning (ML) and computed conversion-based AVO analysis: a study from Jaisalmer Sub-basin, India

Amplitude variation with offset (AVO) analysis is an important tool for identifying natural gas-bearing reservoirs. The changes in seismic amplitudes based on the variation of density and velocity of the rock matrix are captured through the AVO analysis. The current work was performed in the Ghotaru region of the Jaisalmer Sub-basin area, where limited and discrete hydrocarbon discoveries were observed from the Lower Goru Formation during the earlier various exploration campaigns. The discrete nature of the reservoir lithofacies developed challenging scenarios for the successful exploratory campaign. The campaign encountered more difficulties because of limited advanced datasets, which affected the study to capture the extension of hydrocarbon-bearing reservoir lithofacies and its characterization towards a successful exploration campaign. This study shows the way to overcome these challenges using an existing conventional dataset. The study shows the possibility of AVO analysis using a post-stack seismic dataset. A unique conversion method from post-stack to pre-stack seismic is introduced in this study based on the uses of the integrated velocity model. An integrated, robust velocity model was developed with consideration of anisotropy factors. Introducing a machine learning-based algorithm in the petrophysical study, including the conventional approach, provides a robust validation of this work. Intercept (A) and gradient (B) are the basic outcome of AVO analysis. The well-based study and AVO analysis based on intercept (A) and gradient (B) complement each other for finding hydrocarbon-bearing reservoir rock. Cross-plots and AVO analysis show the reservoir's lithofacies extension and fluids. The study reveals the potential of natural gas retained in the Lower Goru Formation, which is composed of patchy sandstone. Two AVO classes (Class I and Class III) of gas-bearing sandstone have been identified in this study. This study presents a unique method for identifying natural gas reservoirs with limited old conventional data.

The effect of lamina and lithofacies assemblage on molecular maturity of oil in a shale source-rock reservoir

This paper discusses the effect of the source rock–reservoir assemblage and the assemblage–induced variability in hydrocarbon expulsion and the subsequent molecular composition as well as molecular thermal maturity within a hybrid shale system. The characterization work was conducted on lithofacies and microlamina scales using core samples from the Chang 73 sub-member of the Triassic Yanchang Formation in the Ordos Basin, China. Samples were collected from a narrow interval with a depth range of less than 15 m, and the main characterization work was performed by Rock-Eval pyrolysis and GC–MS analysis. The results show that chemical fractionation of preferential expulsion and migration of the saturated fraction exists in the source rock–reservoir assemblages at both the lithofacies and lamina scales. However, the molecular composition behaves differently at the lithofacies and lamina scale's source rock–reservoir assemblages, in which ∑C21−/∑C22+ is higher in lamina scale reservoir but lower in lithofacies scale reservoir. It is assumed that the low-molecular weight n-alkanes also follow molecular fractionation. The lithofacies reservoir has a lower ∑C21−/∑C22+ because of the strong storage capacity of the laminated micro-reservoir within shale, which prevents the newly generated lighter oil from being charged into the lithofacies reservoir. The variation trends of thermal maturity indices Ts/hopane, the relative pregnane content, and TA(I)/TA(I + II) ratios, which have the same chemical basis with ∑C21−/∑C22+, carry the same maturity signature as ∑C21−/∑C22+. The above profile of the molecular composition and molecular-derived thermal maturity parameters indicate that within the short interval of a shale system where no differences in thermal maturity are expected, chain scission reactions and their derived thermal maturity indicators are very sensitive to source rock and reservoir. In addition, within a shale system, oil is more easily to expel out from the organic-rich lithofacies that are interbedded with organic-lean lithofacies. Oil expulsion may promote both chain cracking of oil and subsequently kerogen decomposition. This may provide geological evidence to explain why the frequent-stacking assemblage of source rock and reservoir lithofacies in a hybrid shale system is an ideal target for shale oil exploration.

Uncertainty Evaluation Based on Bayesian Transformations: Taking Facies Proportion as An Example

Many input parameters in reservoir modeling cannot be uniquely determined due to the incompleteness of data and the heterogeneity of the reservoir. Sedimentary facies modeling is a crucial part of reservoir modeling. The facies proportion is an important parameter affecting the modeling results, because that proportion directly determines the net gross ratio, reserves and sandbody connectivity. An uncertainty evaluation method based on Bayesian transformation is proposed to reduce the uncertainty of the facies proportion. According to the existing data and geological knowledge, the most probable value of the facies ratio and the prior distribution of uncertainty are estimated. The prior distribution of the facies proportion is divided into several intervals, and the proportions contained in each interval are used in facies modeling. Then, spatial resampling is carried out for each realization to obtain the likelihood estimation of the facies proportion. Finally, the posterior distribution of the facies ratio is achieved based on Bayesian transformation. The case study shows that the uncertainty interval of sandstone proportion in the study area has been reduced from [0.31, 0.59] to [0.35, 0.55], with a range reduction of 29%, indicating that the updated posterior distribution reduces the uncertainty of reservoir lithofacies proportion, thereby reducing the uncertainty of modeling results.

Clay mineral transformation mechanism modelling of shale reservoir in Da’anzhai Member, Sichuan Basin, Southern China

Shale reservoirs often undergo intense clay mineral transformation, which plays a crucial role in the formation and evolution of pores. The reservoir lithofacies types of Da’anzhai Member in the Sichuan Basin are complex, the heterogeneity is strong, and the transformation mechanism of clay minerals is unclear, limiting the understanding of reservoir diagenesis and reservoir formation mechanism. In this study, we selected the typical shale reservoir in the Da’anzhai Member of the eastern Sichuan Basin and innovatively introduced the multiphase fluid-chemical-thermal multi-field coupled numerical simulation technique to focus on the dissolution, precipitation and transformation laws of diagenetic minerals in the shale reservoir. We calculated the transformation of diagenetic minerals and their physical response under different temperatures, pressure and fluid conditions and identified the main controlling factors of mineral transformation in shale reservoirs in the study area. The results show that the transformation of smectite to illite in the Da’anzhai Member is a complex physicochemical process influenced by various factors such as temperature, pressure, fluid, and lithology. The increase in temperature can promote illitization until the critical temperature of 110°C–115°C, below which the conversion rate of smectite to illite increases as the temperature increases. However, when it is higher than the critical temperature, the degree of illitization decreases. In specific K-rich fluids, organic acids significantly affect the conversion of clay minerals in the Da’anzhai Member of the formation. The acidic fluid promotes the dissolution of minerals such as K-feldspar and releases K+, thus provides the material basis for illitization. The research results provide theoretical support for the diagenetic and formation mechanism of the shale reservoir in the Da’anzhai Member of the Sichuan Basin and even for the efficient exploration and development of shale gas.

Ultradeep carbonate reservoir lithofacies classification based on a deep convolutional neural network — A case study in the Tarim Basin, China

Lithofacies identification is essential in reservoir evaluation, especially in ultradeep carbonate reservoirs. In general, coring samples are the best sources to identify carbonate lithofacies because they are taken directly from reservoirs. However, the core is expensive to obtain, and generally its availability is greatly limited. In recent years, deep learning has attracted enormous attention because of its robust nonlinear regression and classification ability. This study applies a deep-learning algorithm to identify the lithofacies using geophysical well-log data. Six types of well-log data, such as natural gamma ray, density (DEN), neutron porosity (CNL), acoustic (AC), and shallow and deep lateral resistivity well logs (RT/RXO), are smoothed by the average sliding method and converted to 2D data. Then, the 2D data are treated as inputs to predict the carbonate lithofacies through the convolutional neural network (CNN). The results indicate that the prediction accuracy rate is 90.2%. This indicates that the CNN can identify different carbonate lithofacies well.

Pore structure characteristics of different lithofacies of the Longmaxi shale, Western Hunan‐Hubei Region, China: Implications for reservoir quality prediction

Lithofacies and micropore characteristics of shale reservoir directly affect the exploration and development of natural gas. To determine the development potential of shales of the Longmaxi Formation in the Western Hunan‐Hubei Region, the lithofacies types and micropore structure characteristics of the Longmaxi Formation shales have been systematically studied in this paper, based on ordinary thin sections, argon ion polishing–scanning electron microscopy, physical property testing, x‐ray diffraction whole‐rock and clay mineral analysis, isothermal nitrogen, and carbon dioxide adsorptions. According to the principle of ‘laminar structure + total organic carbon (TOC) + mineral composition of felsic, clay, and carbonate minerals’, eight types of shale lithofacies were identified. The effective porosity of the Longmaxi shale ranges from 1.8% to 14.4%, with a peak range from 3% to 6%, while the permeability ranges from 0.001 to 1.27 mD, with an average of 0.106 mD. Combined with the isothermal adsorption experiments, the proportion of mesopores is the largest in the Longmaxi shale, followed by macropores and micropores. Moreover, the pore diameters of mesopores are concentrated between 3 and 10 nm, while those of micropores are concentrated between 0.4 and 0.9 nm. The pore volume of shale is most developed when the content of brittle felsic minerals is 50% ~ 75% and the content of clay minerals is 25% ~ 50%, and the TOC content is positively correlated with the pore volume and specific surface area. There are significant differences in the pore development characteristics of different lithofacies. The shale reservoir space analysis indicates that micropores are controlled by TOC content and clay minerals, while the macropores are controlled by depositional intergranular pores, TOC content, and felsic content, as well as diagenetic pyrite mould pores, microfractures pores, and intercrystalline pores. On the other hand, mesopores are related to a mix of depositional and diagenetic factors with dominant contribution of intergranular pores, intercrystalline pores, organic pores, and intergranular shrinkage fractures pores. It is found that the pore development of massive organic‐rich siliceous shale lithofacies has the best reservoir quality, including well‐connected intergranular pores, intercrystalline pores, organic pores, and shrinkage fractures. Therefore, the massive organic‐rich siliceous shale lithofacies is the dominant fine‐grained reservoir lithofacies in the studied area. Based on understating the pore characteristics of different lithofacies, this study can provide a scientific basis for the prediction of favourable sweet spots of shale gas reservoir in the Longmaxi Formation, western Hunan‐Hubei Region.

Discrimination of lithofacies in tight gas reservoir using field-specific rock physics modeling scheme. A case study from a mature field of middle Indus Basin, Pakistan

Discrimination of lithofacies in tight gas reservoir using field-specific rock physics modeling scheme. A case study from a mature field of middle Indus Basin, Pakistan

Lithofacies logging identification for strongly heterogeneous deep-buried reservoirs based on improved Bayesian inversion: The Lower Jurassic sandstone, Central Junggar Basin, China

The strong heterogeneity characteristics of deep-buried clastic low-permeability reservoirs may lead to great risks in hydrocarbon exploration and development, which makes the accurate identification of reservoir lithofacies crucial for improving the obtained exploration results. Due to the very limited core data acquired from deep drilling, lithofacies logging identification has become the most important method for comprehensively obtaining the rock information of deep-buried reservoirs and is a fundamental task for carrying out reservoir characterization and geological modeling. In this study, a machine learning method is introduced to lithofacies logging identification, to explore an accurate lithofacies identification method for deep fluvial-delta sandstone reservoirs with frequent lithofacies changes. Here Sangonghe Formation in the Central Junggar Basin of China is taken as an example. The K-means-based synthetic minority oversampling technique (K-means SMOTE) is employed to solve the problem regarding the imbalanced lithofacies data categories used to calibrate logging data, and a probabilistic calibration method is introduced to correct the likelihood function. To address the situation in which traditional machine learning methods ignore the geological deposition process, we introduce a depositional prior for controlling the vertical spreading process based on a Markov chain and propose an improved Bayesian inversion process for training on the log data to identify lithofacies. The results of a series of experiments show that, compared with the traditional machine learning method, the new method improves the recognition accuracy by 20%, and the predicted petrographic vertical distribution results are consistent with geological constraints. In addition, SMOTE and probabilistic calibration can effectively handle data imbalance problems so that different categories can be adequately learned. Also the introduction of geological prior has a positive impact on the overall distribution, which significantly improves the accuracy and recall rate of the method. According to this comprehensive analysis, the proposed method greatly enhanced the identification of the lithofacies distributions in the Sangonghe Formation. Therefore, this method can provide a tool for logging lithofacies interpretation of deep and strongly heterogeneous clastic reservoirs in fluvial-delta and other depositional environments.

Identification of tight sandstone reservoir lithofacies based on CNN image recognition technology: A case study of Fuyu reservoir of Sanzhao Sag in Songliao Basin

Currently, lithofacies are automatically classified in petroleum reservoirs based on different algorithm models using well-logging vector datasets. However, well-logging vector datasets are challenging for old oil fields where there are only well-logging images dataset present. Nevertheless, tight sandstone reservoirs, which have been neglected, are becoming exploration targets in old oil fields. This has made it necessary to redefine lithofacies characteristics. Thus, this paper describes a novel, widely applicable method for using a convolutional neural network (CNN) to identify tight sandstone reservoir lithofacies, which can handle well-logging image datasets rather than well-logging vector datasets. We applied this method to the Sanzhao Sag in the Northern Songliao Basin of China using the following steps: (1) dividing typical lithofacies based on core and well-logging characteristics; (2) preparing different lithofacies’ well-logging image datasets; and (3) establishing a CNN model with training, testing, and verification. For lithofacies recognition in a tight sandstone reservoir, compared with the recognized result of a model based on a well-logging vector datasets, such as decision tree analysis (DTA; 83%), support vector machine (SVM; 81%), K-nearest neighbor (KNN; 86%), and random forest (RF; 92%), the CNN model has a better prediction accuracy (92%). It also performs well when recognizing new wells in the Sanzhao Sag (92.16%). This result shows that the CNN model can effectively identify tight sandstone reservoir lithofacies based on a well-logging image dataset. Additionally, the sample intervals, lithofacies, geologic characteristics, and sample numbers affecting the prediction accuracy of the CNN model were also analyzed.

Pore throat heterogeneity of different lithofacies and diagenetic effects in gravelly braided river deposits: Implications for understanding the formation process of high-quality reservoirs

Tight gas sandstone is a unique kind of unconventional hydrocarbon resource in that its intricate pore throat structure governs reservoir physical properties and gas reservoir productivity. However, the effect of differential diagenesis of various lithofacies on the heterogeneity of pore throat structure remains unclear. In this paper, a series of experiments were integrated to evaluate pore throat structures and fractal characteristics of the different lithofacies in a tight sandstone gas reservoir, and followed by a study of the impact of diagenetic alteration on them. Results demonstrate that the mineral composition, dominant pore types, pore throat structural parameters, fractal dimension, and physical properties of various lithofacies are notably different, which is mostly attributable to diagenetic alteration variability. There are more primary pores and less clay mineral content in coarser lithofacies. Pebbly coarse-grained sandstone and sandy conglomerate reservoirs that have undergone strong dissolution have more composite pores composed of residual intergranular pores and dissolution pores, which help to form a homogeneous pore throat system with good connectivity, resulting in the best reservoir quality. Due to the strong compaction and high clay mineral content filling pores and throats in medium-grained sandstone and fine-grained sandstone lithofacies, a large number of primary pores were lost, and throats were blocked, which easily formed intercrystalline pores, leading to poor reservoir quality. Coarse-grained sandstone reservoirs with moderate clay mineral content have undergone moderate compaction and dissolution and have moderate reservoir quality. Filamentous illite is the most destructive clay mineral to pore throat connectivity. Intergranular dissolution pores and moldic pores contribute significantly to the physical properties of pebbly coarse-grained sandstone and sandy conglomerate reservoirs. Pebbly coarse-grained sandstone and sandy conglomerate lithofacies are considered high-quality reservoirs with greater development potential for tight gas. This work has important guiding significance for the prediction of high-quality reservoirs in similar tight sandstone reservoirs.

Reservoir lithofacies study of the Toolebuc Formation, Eromanga Basin, Australia as a potential unconventional target

Open file data and new laboratory data were integrated with different lithofacies from gamma-ray (GR) response and core to interpret reservoir properties that would lead to a hydrocarbon potential targeting model for the Toolebuc Formation (Fm). The Toolebuc Fm is divided into an upward deepening cycle (UDC) followed by an upward shallowing cycle (USC), in which silica-rich argillaceous mudstone (Facies 1), mixed argillaceous mudstone (Facies 2) and interbedded calcareous mudstone and shelly thin beds/horizons (Facies 3) occur. Facies 1 is enriched in clay and silica component with relatively low Total Organic Carbon (TOC). Facies 2 has a mixed mineralogical composition with high TOC. Facies 3 is enriched in carbonates (coccoliths and shelly debris). The TOC value is high and shows large variation. The organic matter (OM) in the Toolebuc Fm corresponds to Type II kerogen and exhibits variability in maturity indicators but has arguably entered the early oil window. The dominant pore types of the Toolebuc Fm are intraparticle mineral pores and interparticle mineral pores while OM pores are rare. Samples collected from the basin centre show evidence of hydrocarbon generation.Vertically, the three lithofacies show a cyclic stacking pattern that corresponds to deepening and shallowing in a marine environment. Facies 1 is the transitional interval between the Toolebuc Fm and its adjacent units, the Wallumbilla Fm below and the Allaru Fm above. Facies 2 dominates the upward deepening cycle while Facies 3 dominates the upward shallowing cycle. The stacking of lithofacies corresponds to an upward increase and a following decrease in GR response, TOC, Brittleness Index (BI), and an upward decrease and a following increase in clay content.Facies 3 in the Toolebuc Fm shows the greatest unconventional potential due to the coincidence of the high TOC and BI. Facies 2 shows moderate potential with the high TOC and moderate BI, while Facies 1 is less favourable with the lower TOC and low BI. The target zone is located at the basin centre due to the higher thermal maturity relative to the marginal areas.

Joint use of multiseismic information for lithofacies prediction via supervised convolutional neural networks

Lithology prediction from seismic data is of great significance for sweet-spot detection, reservoir structure delineation, and geologic model building, hence it is important in reducing the risk of exploration and development. Traditional lithofacies prediction methods often are limited by the seismic inversion accuracy and reliability of the rock-physics relationships, which are challenging to be applied in complex reservoirs (such as those containing coal-bearing strata or thin layers). Convolutional neural networks (CNNs) can represent the coupling relationship of seismic characteristics in the time domain through multilayer convolution and effectively manipulate multitype and multidimensional seismic data. Under the framework of a supervised CNN, we jointly integrate prestack seismic gathers (Pre-SGs), seismic inversion results (P-impedance and [Formula: see text] ratio), multiseismic attributes (amplitude-variation-with-offset [AVO] intercept, AVO gradient, instantaneous amplitude, instantaneous frequency, and instantaneous phase), and spectral decomposition attributes (SDA) to predict lithofacies in a complex clastic reservoir interbedded with thin-layer coal. We determine that the fusion model with multiseismic information containing different perspectives and complementary information of seismic data is capable of achieving better prediction performance than only using one type of input feature. In particular, using the proposed methodology, the angle-dependent Pre-SG is essential to decipher the rich information of lithologic details. The models using only poststack seismic data or inversion results cannot reliably describe lithologic details (especially the thin-coal layers). In addition, by including the SDA into model inputs, the model’s ability to recognize thin layers has been further improved but lead to the slight sacrifice of overall prediction accuracy.

Characteristics of Lithofacies in Deep Shale Gas Reservoirs in the Southeast Sichuan Basin and Their Influence on Pore Structure

The characteristics of lithofacies in the shale reservoir of the Wufeng and Longmaxi formations located in the Luzhou gas field were studied using the three-end-member method (mineralogical components). In addition, the microscopic characteristics and pore genesis of different lithofacies were studied and compared using TOC, FESEM, and digital core images. The results of the present study showed the development of five lithofacies: 1) quartz-rich argillaceous shale; 2) quartz/clay-mixed shale; 3) calcareous/clay-mixed shale; 4) calcium-rich argillaceous shale; and 5) clay-rich siliceous shale. The degree of pore development between lithofacies was highly heterogeneous. In addition, data indicated that total shale porosity increased with the increase in TOC. Thus, the pores were mainly related to organic matter. The enrichment of framboidal pyrite in quartz-rich shale (S1, S-3) and mixed shale facies (M-2, M-3) is essential during the formation of high-quality reservoirs. On the other hand, the content of organic matter in clayey shale was low, which does not favor the development of high-quality reservoirs. The differences in lithofacies pore structures are controlled by the sedimentary environment. The strong retention and reduction environment are the most favorable features for the formation of organic-rich siliceous shale lithofacies, which promote the preservation of organic matter and the development of reservoirs.

Geostatistical Inversion of Spectrally Broadened Seismic Data for Re-Evaluation of Oil Reservoir Continuity in Inas Field, Offshore Malay Basin

The application of geostatistics in seismic inversion techniques has been proven somewhat reliable in the delineation of reservoir properties and has recently attracted the attention of many geoscientists. However, there are cases where its prediction returned negative results after drilling. In this research, we re-evaluated a reservoir in Inas Field, whose geostatistical inversion result wrongly predicted sand continuity, resulting in the spudding of a dry hole. When a geostatistical seismic inversion is successfully applied, it provides an increase in seismic resolution and aids the prediction of sand continuity. Although this method relies more on the statistical data from a well because of the limitation of the seismic data in resolving thin geologic features, the spatial variation of reservoir parameters still depends on seismic data, which often have poor resolution quality. Therefore, to investigate the impact of bandlimited data on the geostatistical inversion, we harmonically extended the seismic bandwidth by applying a sparse-layer spectral inversion algorithm to the data. This algorithm increased the seismic data bandwidth from 80 Hz to 180 Hz, and its tuning thickness reduced from 32 m to 10 m at the reservoir interval. The resultant broadband (180 Hz), as well as the original seismic (narrowband of 80 Hz) data, were both used as input to build two separate geostatistical prediction models, respectively. Twenty (20) realizations of these models were generated, ranked into P10, P50, and P90, and the best case was selected for interpretation. These realizations were used to characterize the reservoir lithofacies distribution. When compared, the result of the broadband inversion, facies and sand distribution model showed that the reservoir facies changed towards the location of the dry well. The broadband geostatistical inversion efficiently improved the reservoir characterization process by not only producing an accurate estimation of the lateral extent of the reservoir heterogeneities but also generating outcomes that help us understand why other geostatistical inversion analyses of the target reservoir were misleading. Contrary to the popular assumption, it was discovered that the tuning effects of bandlimited data could affect the result of a geostatistical inversion and result in wrong facies predictions.

Uncertainty Evaluation Method and Application of Reservoir Lithofacies Proportion under the Condition of Few Wells

Abstract The proportion of different lithofacies is essential to determine the net-gross ratio, which directly affects the calculation of oil and gas reserves. Under the condition of few wells, the reliability of different lithofacies proportions obtained using well data is poor because it is difficult to estimate the uncertainty variation range of different lithofacies proportions. The present study overcomes this problem using an uncertainty evaluation method for reservoir lithofacies proportion. First, different lithofacies proportions of the reservoir are determined using the well and seismic data and are considered as the most likely estimated. Based on the lithofacies proportion, the lithofacies models of the study area are developed. Second, uniform and random methods are applied to sample the lithofacies models, and then, the probability distribution functions of different lithofacies proportions are derived. Third, the three levels of lithofacies proportions in the study area are calculated using the method of quantile and discretization of the probability distribution. Finally, we take M gas field as a sample and compare the test results of the fixed and random well structures. The results show that the sampling results of the fixed well structure model significantly deviate under the influence of the extension direction of the channel and well pattern distribution. Therefore, it is suggested to use random well structure to determine the uncertainty distribution range of lithofacies proportion.

Reservoir Lithofacies Study of the Toolebuc Formation, Eromanga Basin, Australia as an Unconventional Target

Reservoir Lithofacies Study of the Toolebuc Formation, Eromanga Basin, Australia as an Unconventional Target