Lithological Model Research Articles

Causes and Evolution of High Injection–Production Ratios in Low-Permeability Reservoirs: The Role of Water Absorption in Barrier and Intercalated Layers

During the waterflood development of low-permeability reservoirs, the lithology of barrier and intercalated layers adjacent to the reservoir, with specific permeability and porosity, has a significant impact on water injection efficiency and reservoir energy recovery. However, current research on injection–production parameters and pressure changes in low-permeability reservoirs has not fully considered the effect of these barrier layers. Therefore, this study focuses on the Chaoyanggou Oilfield, a typical low-permeability reservoir, aiming to reveal the influence of water absorption by barrier layers on water injection efficiency and pressure changes in the reservoir. The study systematically analyzes the evolution of the injection–production ratio at different development stages by constructing a comprehensive lithological geological model and applying numerical simulation methods. It explores how the water absorption characteristics of barrier layers affect reservoir pressure and injection efficiency. The results demonstrate that argillaceous siltstone and silty mudstone have significant water absorption effects on injected water, critically influencing pressure distribution and fluid flow dynamics in the reservoir. As the water cut increases, the injection–production ratio gradually stabilizes, and the elastic water storage in the reservoir becomes crucial for establishing an effective oil displacement system. The water absorption of barrier layers accounts for 30% to 40% of the injected water. A high injection–production ratio alone does not lead to rapid energy recovery or increased production. Only by balancing the injection–production ratio, reservoir pressure, and water absorption in barrier layers can the efficiency and recovery rate of waterflood development in low-permeability reservoirs be further improved.

Advanced machine learning artificial neural network classifier for lithology identification using Bayesian optimization

Identifying lithology is crucial for geological exploration, and the adoption of artificial intelligence is progressively becoming a refined approach to automate this process. A key feature of this strategy is leveraging population search algorithms to fine-tune hyperparameters, thus boosting prediction accuracy. Notably, Bayesian optimization has been applied for the first time to select the most effective learning parameters for artificial neural network classifiers used for lithology identification. This technique utilizes the capability of Bayesian optimization to utilize past classification outcomes to enhance the lithology models performance based on physical parameters calculated from well log data. In a comparison of artificial neural network architectures, the Bayesian-optimized artificial neural network (BOANN) demonstrably achieved the superior classification accuracy in validation and significantly outperformed a non-optimized wide, bilayer, and tri-layer network configurations, indicating that incorporating Bayesian optimization can significantly advance lithofacies recognition, thus offering a more accurate and intelligent solution for identifying lithology.

Seal and reservoir risk evaluation using hierarchical clustering analysis with seismic attributes in Northwestern Australia

Assessing the presence and quality of reservoir rocks and their sealing capacity is crucial for various applications, including hydrocarbon, geothermal, and CO2 sequestration projects. Typically, exploration geoscientists rely on seismic attributes and borehole logs into interpretation to integrate diverse data for estimating reservoirs and seals. However, for all seismic interpreters, the process is time-consuming.In this study, we explore the application of Hierarchical Clustering Analysis (HCA), an unsupervised machine learning technique, to streamline the integration of multidisciplinary information. While HCA and similar techniques may occasionally misclassify critical data, we demonstrate how to enhance their accuracy by carefully selecting the number of clusters and their calibration with borehole data.The novelty of our work is the innovative transformation of HCA clusters into a 3D lithology model, which can significantly facilitate the estimation of reservoir rock and seal-rock juxtaposition risk. Using the HCA clustering hierarchy, five clusters effectively discern the presence and quality of seal and reservoir rock in two different datasets. The classification, in combination with the fault probability, addresses the seal risk offshore the Northern Carnarvon Basin.

Modeling Groundwater Levels in California's Central Valley by Hierarchical Gaussian Process and Neural Network Regression

AbstractModeling groundwater levels continuously across California's Central Valley (CV) hydrological system is challenging due to low‐quality well data which is sparsely and noisily sampled across time and space. The lack of consistent well data makes it difficult to evaluate the impact of 2017 and 2019 wet years on CV groundwater following a severe drought during 2012–2015. A novel machine learning method is formulated for modeling groundwater levels by learning from a 3D lithological texture model of the CV aquifer. The proposed formulation performs multivariate regression by combining Gaussian processes (GP) and deep neural networks (DNN). The hierarchical modeling approach constitutes training the DNN to learn a lithologically informed latent space where non‐parametric regression with GP is performed. We demonstrate the efficacy of GP‐DNN regression for modeling non‐stationary features in the well data with fast and reliable uncertainty quantification, as validated to be statistically consistent with the empirical data distribution from 90 blind wells across CV. We show how the model predictions may be used to supplement hydrological understanding of aquifer responses in basins with irregular well data. Our results indicate that on average the 2017 and 2019 wet years in California were largely ineffective in replenishing the groundwater loss caused during previous drought years.

Calculation of gaseous storage and Building A 3D Geological model for Jeribe Formation in Mansuriya gas field in Diyala governorate

Al-Mansuriya gas field is considered one of the important gas fields in Iraq because of its good economic gas reserves, The major gas reserves of the Mansuriya field are situated in the Jeribe formation, The period of formation deposition dates back to the lower middle Miocene. The present study aims to build the three-dimensional geological model for Jeribe Formation in Mansuriya Gas Field using (Petrel) software. Drawing on well logs exported from Techlog software in LAS format. Four wells have been chosen in the mansuriya gas field (MN_1, MN_2, MN_3, MN_4) to construct Structural and petrophysical (effective porosity, water saturation and lithology) models. After creating zones for Jeribe Formation, which was divided into 7 zones J1, J2, J3, J4, J5, J6 and J7 zone, Based on the final results of computer processing interpretation CPI was imported from techlog software. The three-dimensional grid of the Jeribe Formation model that was built consists of (10494848) cell and each cell have dimensions I =272 * J = 371 * K =104. Petrophysical models (effective porosity, water saturation and lithology) had been constructed for each zone of the Jeribe Formation showing the J2, J3, J4 and J5 are important reservoir units in terms of their good properties (high effective porosity, low water saturation) and Two types of lithology appear (dolomite and limestone) in different proportions to each unit. The results showed that Units J1 and J6 are non-reservoir units that do not contain gas because they consist of layers of anhydrite with very low porosity. The mn-4 well showed different results from the rest of the wells, and none of its units can’t be considered a reservoir due to the large water saturation volume.

Horizon-assisted lithologic modeling: understanding Mississippi Embayment and Coastal Lowlands aquifer systems in Louisiana and southwestern Mississippi, USA

Horizon-assisted lithologic modeling: understanding Mississippi Embayment and Coastal Lowlands aquifer systems in Louisiana and southwestern Mississippi, USA

Three-Dimensional Geologic Modeling of the Kiruna Mining District, Sweden: Insights into the Crustal Architecture and Structural Controls on Iron Oxide-Apatite Mineralization

Abstract To support economic decisions and exploration targeting, as well as to understand processes controlling the mineralization, three-dimensional structural and lithological boundary models of the Kiruna mining district have been built using surface (outcrop observations and measurements) and subsurface (drill hole data and mine wall mapping) data. Rule-based hybrid implicit-explicit modeling techniques were used to create district-scale models of areas with high disproportion in data resolution characterized by dense, clustered, and distant data spacing. Densely sampled areas were integrated with established conceptual studies using geologic conditions and the addition of synthetic data, leading to variably constrained surfaces that facilitate the visualization, interpretation, and further integration of the geologic models. This modeling approach proved to be efficient in integrating local, frequently sampled areas with district-scale, sparsely sampled regions. Dominantly S-plunging lineation on N-S–trending fracture planes, characteristic fracture mineral fill, and weak rock mass at the ore contact indicated by poor core orientation quality and rock quality description suggest that ore-parallel fractures in the Kiirunavaara area were more commonly reactivated. Slight variation in the angular relationship of fracture sets situated in different fault-bounded blocks suggests that strain accommodation across the orebodies was uneven. The location of brittle faults identified in drill core, deposit-scale structural analysis, and aeromagnetic geophysical maps indicate a close relationship between fault locations and the iron oxide-apatite mineralization, suggesting that uneven stress accommodation and proximity of conjugate fault sets played an important role in juxtaposing blocks from different crustal depths and control the location of the iron oxide-apatite orebodies.

Borehole‐Based Interval Kriging for 3D Lithofacies Modeling

AbstractDeveloping a three‐dimensional (3D) lithofacies model from boreholes is critical for providing a coherent understanding of complex subsurface geology, which is essential for groundwater studies. This study aims to introduce a new geostatistical method—interval kriging—to efficiently conduct 3D borehole‐based lithological modeling with sand/non‐sand binary indicators. Interval kriging is a best linear unbiased estimator for irregular interval supports. Interval kriging considers 3D anisotropies between two orthogonal components—a horizontal plane and a vertical axis. A new 3D interval semivariogram is developed. To cope with the nonconvexity of estimation variance, the minimization of estimation variance is regulated with an additional regularization term. The minimization problem is solved by a global‐local genetic algorithm embedded with quadratic programming and Brent's method to obtain kriging weights and kriging length. Four numerical and real‐world case studies demonstrate that interval kriging is more computationally efficient than 3D kriging because the covariance matrix is largely reduced without sacrificing borehole data. Moreover, interval kriging produces more realistic geologic characteristics than 2.5D kriging, while conditional to spatial borehole data. Compared to the multiple‐point statistics (MPS) algorithm—SNESIM, interval kriging can reproduce the geological architecture and spatial connectivity of channel‐type features, meanwhile producing tabular‐type features with better connectivity. Because the regularization term constrains kriged value toward 0 or 1, interval kriging produces more certainty in sand/non‐sand classification than 2.5D kriging, 3D kriging, and SNESIM. In conclusion, interval kriging is an effective and efficient 3D geostatistical algorithm that can capture the 3D structural complexity while significantly reducing computational time.

Structural and lithological model of the Glukhivetsky deposit of eluvial kaolins

Structural and lithological model of the Glukhivetsky deposit of eluvial kaolins

A new insight for geophysical and geospatial mapping quaternary alluvium of Indo-Gangetic plains for exploration and assessment of groundwater in Haryana, India

A comprehensive study on groundwater exploration and assessment was conducted, integrating geophysical, geological, lithological, and resistivity logging techniques for a holistic evaluation of groundwater resources in Haryana, India. The primary research objective was to gain insight into the geological composition of alluvial sand and its implications for groundwater occurrence in Haryana's semi-arid region. The study showcases high-resolution electrical resistivity tomography results alongside conceptual cross-sectional models and 3D lithological models, revealing buried channels that serve as groundwater sources and saturated sand as well as recharge conduits within the alluvial region of Haryana. Moreover, borehole drilling at two sites and resistivity logging data demonstrate a strong correlation between various lithologies and resistivity logs. Overall, groundwater prospect zones are delineated at depths ranging from 18 m to over 100 m based on resistivity values of aquifer zones inferred from electrical resistivity tomography models, as well as mapped thick sand layers with significant hydrogeological importance in the study area. This emphasizes the stress on the aquifer system, where groundwater is being extracted from deeper depths. The conceptual litho-sectional model reveals a thick sand signature at intermediate depths, indicating the presence of buried paleochannels extending from Galedwa (Kurukshetra) to Jubbal (Yamunanagar). Additionally, the 3D lithological models, along with the electrical resistivity tomography results, facilitate strategic planning for shallow and deep boreholes for groundwater exploration and prospecting, aiming at sustainable groundwater development and management of resources in both the present and future.

Successive bootstrapping deep learning approach and airborne EM-borehole data fusion to understand salt water in the Mississippi River Valley Alluvial Aquifer

Increasing demands from agriculture and urbanization have decreased groundwater level and increased salinity worldwide. Better aquifer characterization and soil salinity mapping are important for proactive groundwater management. Airborne electromagnetic (AEM) is a powerful tool for aquifer characterization and salinity delineation. However, AEM needs to be interpreted with caution before being used for groundwater quality analysis. This study introduces a framework that utilizes the AEM data for both lithologic modeling and salinity delineation. A resistivity-to-lithology (R2L) model is developed to interpret AEM resistivity to lithology based a depth-dependent multi-resistivity thresholds. Then, a cokriging method is used to integrate AEM data from two different EM systems to predict resistivity at the aquifer. Finally, a resistivity-to-chloride concentration (R2C) model utilizes the resistivity model to estimate chloride concentrations at sand facies. A deep learning artificial neural network (DL-ANN) model is introduced with a successive bootstrapping approach to estimate total dissolved solids first and then use it together with resistivity data to estimate chloride concentration. The methodology was applied to delineating salinity plumes in the Mississippi River Valley alluvial aquifer (MRVA). This study found that the salinity distribution in MRVA is highly correlated with the Jurassic salt basin, salt domes, faulting, seismicity, and river water quality. The result indicates salinity upconing due to excessive pumping.

Литолого-фациальная модель бакинских отложений Астраханского Прикаспия

The influence of fluctuations in the Caspian Sea level on the lithological and facies characteristics of sedimentary deposits is analyzed. Special attention is paid to the study of periodic changes in the water level in the Caspian Sea during the Absheron and Baku time in order to determine the effect of fluctuations on sedimentation processes. The Turanian regression was considered, and its differentiation with the Absheron and Baku transgressions was carried out. The study included the study of climatic conditions of past eras and their impact on the diversity of flora and fauna, which, in turn, had an impact on sedimentation processes in various periods of the Baku stage. Geological and statistical methods were used to determine the characteristics of reservoirs in sediments, as well as to study their distribution over the territory. A lithological and facies model has been constructed that reflects two cycles of regression and transgression characteristic of the deposits of the Baku time, which made it possible to describe the distribution of sandy and clay facies in various sections of the section and interpret a map of sandiness to identify buried river valleys. It is concluded that two lithofacies bundles reflecting the transgressive-regressive cycle of the Great Caspian Sea were identified in Baku time. Hypotheses have been formed according to which the reservoirs were formed in the final stage of the Turkic regression and were later covered with sediments of the Baku transgression. It has been established that these reservoirs play a significant role for gas accumulations and are distributed almost throughout the territory of the Astrakhan Caspian Sea. The identification of areas with a high reservoir content may be important for future exploration in this region.

Borehole analysis procedure for constructing a lithological model of natural barrier and its application to the KAERI underground research tunnel

Borehole analysis procedure for constructing a lithological model of natural barrier and its application to the KAERI underground research tunnel

Estimation of rock physics properties via full waveform inversion of vertical seismic profile data recorded by accelerometer and fibre-optic sensors

SUMMARY Combining elastic full waveform inversion (FWI) with rock physics holds promise for expanding the application of FWI beyond seismic imaging to predicting and monitoring reservoir properties. Distributed acoustic sensing (DAS), a rapidly developing seismic acquisition technology, is being explored for its potential in supporting FWI applications. In this study, we implement a sequential inversion scheme that integrates elastic FWI and Bayesian rock physics inversion, using a vertical seismic profile (VSP) data set acquired with accelerometer and collocated DAS fibre at the Carbon Management Canada’s Newell County Facility. Our aim is to establish a baseline model of porosity and lithology parameters to support later monitoring of CO2 storage. Key strategies include an effective source approach for addressing near-surface complications, a modelling strategy to simulate DAS data comparable to field data, and a Gaussian mixture approach to capture the bimodality of rock properties. We conduct FWI tests on accelerometer, DAS, and combined accelerometer-DAS data. While our inversion results accurately reproduce either data set, the elastic models inverted from accelerometer data outperform the other two in matching well logs and identifying the target reservoir. We attribute this outcome to the limited complementarity of DAS data with accelerometer data in our experiment, along with the limitations imposed by single-component measurements on DAS. The porosity and lithology models predicted from accelerometer-derived elastic models are reasonably accurate at the well location and exhibit geologically meaningful spatial distribution.

Prediction of igneous lithology and lithofacies based on ensemble learning with data optimization

Igneous rocks are widely developed in various Mesozoic and Cenozoic continental and marine basins. Igneous reservoirs are the key reservoirs for current oil and gas development. Accurate prediction of lithology and lithofacies is a prerequisite for the effective exploration of igneous reservoirs. Igneous lithology and lithofacies are complex and correlated. The existing single-label igneous rock identification methods only consider the prediction of individual properties, and less consideration is given to the correlation of reservoir properties. Therefore, lithology and lithofacies prediction based on conventional logging data is regarded as a typical class-imbalanced multilabel classification problem when considering both attribute correlation in algorithms and evaluation metrics. To solve this problem, an ensemble method of data optimization combined with multigrained cascade forest (CF) is used in this study to develop a new multilabel lithology and lithofacies prediction model based on data from nine conventional logs in the eastern depressional reservoirs of the Liaohe Basin, and satisfactory results are obtained. The imbalance problem of the conventional logging data sets is first solved by using K-means and synthetic minority oversampling technique methods; then, the model is trained by scenario transformation and stripping with multigrained CF; and next, a multilabel classification evaluation index with multiple perspectives is introduced. The differences between the model and typical intelligent algorithms such as CF, adaptive boosting, random forest, and support vector machine are compared in simulated wells, and the new model is found to have obvious advantages. The model is finally applied to an actual well, and the accurate prediction results illustrate that the new model designed and trained for the class imbalance multilabel classification problem in this paper has application value in the lithology and lithofacies multilabel prediction of igneous rocks and also provides a theoretical basis for more complex multilabel reservoir evaluation using machine learning in the future.

Influence of Lithology on the Characteristics of Wave Propagation and Dynamic Response in Rocky Slope Sites Subject to Blasting Load Via the Discrete Element Method

Abstract To investigate the dynamic response and attenuation law of rock slope sites subjected to blasting, three lithological numerical models, including slate (hard rock), tuff (relatively soft rock), and shale (soft rock), are established by using MatDEM. By analyzing the wave field, velocity, and acceleration response of the models and their Fourier spectrum, combined with stress and energy analysis, their dynamic response characteristics are investigated. The results show that blasting waves propagate from near field to far field in a circular arc, and the attenuation effect of waves in soft rock is less than that in hard rock. The influence of lithology on the dynamic response of the ground surface and bedrock is different. Blasting waves mainly affect the dynamic response in the near-field area of the blasting source. In addition, the dynamic amplification effect of slopes is as follows: hard rock > relatively soft rock > soft rock. The slope surface has an elevation attenuation effect. A dynamic amplification effect appears in the slope interior within the relative elevation (0.75, 1.0). The Fourier spectrum has an obvious predominant frequency, and that of the slope crest and interior is less than that of the slope surface. Moreover, the total energy generated by the rocky sites gradually changes into kinetic energy, gravitational potential energy, elastic potential energy, and heat. Energy-based analysis shows that the attenuation effect of blasting waves in hard rock is larger than that in soft rock overall. This work can provide a reference for revealing the blasting vibration effect of rock sites.

A Lithological Classification Model Based on Fourier Neural Operators and Channel-Wise Self-Attention

A Lithological Classification Model Based on Fourier Neural Operators and Channel-Wise Self-Attention

Application of the electrical resistivity method and the estimation of limestone volume: a case study

The present work used the electrical resistivity approach to conduct a three-dimensional modeling and initial volume estimation of the limestone layer in the Mintom region located in southern Cameroon. In order to achieve the objectives of the study, a total of 21 electrical soundings spaced 250 m were first collected in the field using the Schlumberger array. These soundings were conducted along three profiles oriented in an east–west direction, spaced 500 m. Additionally, a geological survey was conducted to identify and emphasize the presence of limestone formations within the designated study region. The interpretation of the sounding data was conducted based on the analysis of the sounding curves. The interpretation outcomes, specifically resistivity and thickness, were compared with the geological field data, resulting in the development of lithostratigraphic logs for each sounding. The geological sections were constructed using the logs of the designated profile. The lithological logs were utilized to establish a lithological interface model and calculate the volume of the limestone layer at 260 ± 13 × 106 m3, utilizing the inverse distance method built into RockWorks software. A resistivity value is assigned to each geological layer in a sounding curve, allowing for the development of a resistivity variation model specific to the limestone layer. The proposed model facilitates the categorization of limestone layers based on their resistivity variations, thus serving as a fundamental reference for prospective exploratory activities within the designated study region. Our integrated approach provides a replicable model for a better understanding of the limestone reserve and effective management of this valuable resource.

Polychronic-polygenic spatial-paragenetic ilmenite bearing of the Bukinska area of the Mezhyrichny deposit of titanium ores

Introduction. The primary task, aimed at meeting the needs of our own titanium raw materials, is to put into operation as soon as possible the deposits in which spatially and paragenetically different ore bearing capacity is combined in a spatial and paragenetic way and which have sufficient detail been studied. One of these deposits is Mezhyrichne, which is located within the Volyn megablock, in the central part of the Korosten pluton, Zhytomyr region. Within the deposit, several areas have been identified, including Bukinska. The purpose of the publication. To investigate the ilmenite ore-bearing of the polygenic-polychronic spatial-paragenetic ore-bearing system within the Bukinska area, which is composed of ore-bearing rocks of the foundation, their weathering crust, continental alluvial (Aptian-Lower Albian) deposits (formed due to erosion and redeposition of eluvium) and coastal-sea (Turonian) formations (formed due to erosion and redeposition of Lower Cretaceous alluvium and partially weathering crust). Materials and Methodology. The methodical and methodological basis of the research was the work of the autors on the structural and lithological modeling of placers of heavy minerals. The actual material for the studies were the production reports and scientific publications. A target database was created for cartographic modeling of the structure and quality indicators (distribution of ilmenite content along the lateral spread and vertical cross-sections of wells) of the rocks. The database contains the coordinates of 1635 wells, their description, test results. Cartographic constructions were made in Inkscape, Golden Software Strater, and Golden Software Surfer software. Correlations between certain parameters of ore-bearing sediments were studied in Microsoft Excell. Main Results. Information on the geological structure of the Bukinska area of the Mezhyrichne titanium ore deposit is given. It has been found that the ore-bearing potential of the area is determined by a polygenic-polychronic spatially-paragenetically connected ore system. This ore system is composed of titanium-bearing rocks of the crystalline basement of the Volodarsk-Volyn complex, their weathering crusts, Lower Cretaceous continental, Upper Cretaceous coastal – marine products of erosion and redeposition of eluvium and to a lesser extent and partially heterogeneous formations of the Quaternary system. Maps of the relief of the bottom, the top surface and thickness of the ore-bearing rocks have been constructed. The lateral distribution of the average ilmenite content in all rock types was studied. The directionality and strength of correlations were investigated. The peculiarities of the distribution of ilmenite in the vertical section of formations of different ages and different genesis have been clarified. Conclusions. A target database was created, on the basis of which a set of maps was built, which made it possible to study the structural and material parameters of ore-bearing deposits. The ore-bearing potential of the polygenic-polychronic spatial-paragenetic system of the Bukinska area was qualitatively and quantitatively assessed. It has been found that the Bukinska area, within which there is a spatially and paragenetically combined temporal and heterogeneous ore bearing, has a significant ore-bearing potential and is attractive for investment. The obtained results are an information base for supporting mining operations.

Refined lithology identification: Methodology, challenges and prospects

Refined lithology identification is the reliable basis for determining the spatial distribution of lithology, reserve estimation and 3D modeling. With the development of artificial intelligence, there have been some intelligent identification methods that can identify coarse-grained classes of rock. However, the research progress of fine-grained lithology identification is slow due to the difficulty of fine-grained identification, the high cost of data collection and complexity of engineering site conditions. Against this background, we conducted research on fine-grained identification based on macroscopic images, analyzed the differences between fine-grained identification and coarse-grained identification, explored the differences between image identification under laboratory conditions and on-site conditions, and discussed the development direction of lithology identification. Specifically, we constructed a laboratory dataset (160 lithologies) and an on-site dataset (13 lithologies); then, we developed a classification model specifically for extracting lithological features; next, we conducted identification experiments using the self-built model and detection model on these two datasets and comparative analysis was carried out. The results show that the self-developed model has an evaluation metric (F1-score) of 0.9764 on the images collected in the laboratory, but an F1-score of only 0.6143 on the images collected in the engineering site. Unlike coarse-grained identification, image augmentation that changes local features degraded the fine-grained lithology identification performance. Factors such as surface contamination caused by geological process, weathering and similarity in appearance due to metamorphism in the engineering site can affect the accuracy of image-based methods. This study explores in detail the potential and existing problems of fine-grained lithology identification based on macroscopic images, which can provide reference for future reservoir-related issues.