Petrophysical Properties Research Articles

Permeability Prediction from Well Log Data Using Artificial Neural Networks: A Case Study of the Hawaz Formation in D-Field, Libya

Permeability is an important parameter in the characterization of any hydrocarbon reservoir. Yet, despite its vital importance, it is one of the most difficult and controversial petrophysical properties to calculate accurately due to nonlinearity and uncertainty in the dataset. Formation permeability is often measured in the laboratory from cores or evaluated from well test data. Core analysis and well test data, however, are only available from a few wells in a field, while the majority of wells are logged. The aim of this paper is to design an artificial neural network (ANN) model to predict formation permeability using a dataset from the Hawaz Formation in the D-field NC-186 concession, East Murzuq Basin, Libya. In this study, a back-propagation neural network (BP-ANN) model was built using the Python programming language. A total of 950 core horizontal permeability measurements and their corresponding well log data were collected from four wells to build the model. Traditional statistical analysis of the porosity/permeability relationship in cored well data yielded no reliable correlations for predicting permeability in uncored wells with R2 less than 15%. A supervised BP-ANN model was trained successfully and was successfully able to predict the permeability of the formations. Despite the presence of high reservoir heterogeneity, the permeability profile predicted by the ANN model using well log data agrees well with core permeability, which clarifies the applicability of this method.

Performance of the Petroleum Reservoir Under Waterflooding at the Petrochad Field in Mangara-Chad

The Mangara petroleum field is made up of three superimposed reservoirs (C, D, E) with approximately 2500 m depth. These are unconsolidated sandstone reservoirs with an underlying active aquifer. The petrophysical properties of the reservoirs are relatively good as it’s a self-sourced unconventional reservoir based on its organic richness characteristics unconventional resource opportunity as tight carbonate reservoir. Tank pressure is approximately 2900 psi, for an average temperature of 180°F. Of the 50 wells initially drilled, 23 are currently producing. The pressure is support by 04 injector wells, and daily production is estimated at approximately 12,000 bbl/d. To maximize oil production and minimize water production, the positions of injection wells were moved, which permitted to reduce the number of producing wells from 23 to 18. The result revealed that this scheme can maintained the pressure at desired levels of 2900 psi, until year 2040 and reversed the current trend in water production, which was 14,000 bbl/d. In the predicted scenarios, cumulative production will be maintain at 25,000 bbl/d with a daily production of around 18,000bbl of oil compared to formerly figure of 7000 bbl of water until 2040.

Characterization of petrophysical and seismic properties for CO2 storage with sensitivity analysis

Saline aquifers are considered as highly favored reservoirs for CO2 sequestration due to their favorable properties. Understanding the impact of saline aquifer properties on the migration and distribution of CO2 plume is crucial. This study focuses on four key parameters—permeability, porosity, formation pressure, and temperature—to characterize the reservoir and analyse the petrophysical and elastic response of CO2. First, we performed reservoir simulations to simulate CO2 saturation, using multiple sets of these four parameters to examine their significance on CO2 saturation and the plume migration speed. Subsequently, the effect of these parameters on the elastic properties is tested using rock physics theory. We established a relationship of compressional wave velocity (Vp) and quality factor (Qp) with the four key parameters, and conducted a sensitivity analysis to test their sensitivity to Vp and Qp. Finally, we utilized visco-acoustic wave equation simulated time-lapse seismic data based on the computed Vp and Qp models, and analysed the impact of CO2 saturation changes on seismic data. As for the above numerical simulations and analysis, we conducted sensitivity analysis using both homogeneous and heterogeneous models. Consistent results are found between homogeneous and heterogeneous models. The permeability is the most sensitive parameter to the CO2 saturation, while porosity emerges as the primary factor affecting both Qp and Vp. Both Qp and Vp increase with the porosity, which contradicts the observations in gas reservoirs. The seismic simulations highlight significant variations in the seismic response to different parameters. We provided analysis for these observations, which serves as a valuable reference for comprehensive CO2 integrity analysis, time-lapse monitoring, injection planning and site selection.

Betung Field Development Based On Petrophysical Integration Analysis And Radius Drainage

Abstract Betung Field which has a productive reservoir of sandstone layers of the Air Benakat Formation. The potential in Layer-5 (L-5) in 2018 is still quite large, with Original Oil In Place (OOIP) of 16,531.05 MSTB. This study uses Petrel Petrophysical and Oil Field Manager software. The output of Petrel Petrophysical is in the form of a petrophysical property model. The output of the Oil Field Manager is a Bubble map model to determine the cumulative production of a well. The petrophysical property model is then integrated with the Bubble map model to find locations for additional potential development wells. Based on the petrophysical distribution map, the Southeast – South area has reference wells with good quality where the porosity value is excelent (30% - 31%), permeability is good (119 mD – 139 mD) and water saturation is small (28% - 38%). Based on the map of the distribution of the drainage radius (Bubble map), the reference wells in the Southeast – South area have large draining radius (45 m/147.64 ft – 70 m/229.67 ft) with large cumulative (3513 STB – 10723 STB). Based on the petrophysical overlay results and drain radius, indicating the Southeast – South area potential for field development with the addition of further wells.

Evaluation of Petrophysical Properties of Mishrif, Rumiala, Ahmadi, and Mauddud Formations in Nasiriya Oil Field—Middle of Iraq

Evaluation of Petrophysical Properties of Mishrif, Rumiala, Ahmadi, and Mauddud Formations in Nasiriya Oil Field—Middle of Iraq

Geophysical evaluation and petrophysical assessment of the Abu Roash "F" Member: A probable unconventional oil reservoir in Heba Field, eastern Abu Gharadig Basin, Egypt

This study investigates the geophysical properties of the Abu Roash F Member (AR/F) as a potential unconventional oil reservoir in Heba Field, located in the eastern region of the Abu Gharadig Basin (Egypt). Analysis of seismic data reveals the influence of Late Cretaceous dextral wrenching tectonic activity, evidenced by the presence of ENE-WSW anticlinal folds and intersecting NW-SE as well as WNW-ESE extensional faults throughout the study area. This tectonic regime is believed to have played a significant role in the formation of micro-fractures within the AR/F carbonate, which can enhance the reservoir's potential by improving its permeability and porosity. Petrophysical assessment identifies favorable reservoir zones within specific depth intervals across multiple wells. These zones exhibit promising reservoir quality, characterized by low shale volume (below 0.20), which indicates a cleaner reservoir rock with less clay content. Additionally, the high total porosity (ranging from 0.20 to 0.25) and moderate effective porosity (ranging from 0.15 to 0.20) suggest that a substantial portion of the rock's pore space is available for storing hydrocarbons. The low water saturation (between 0.40 and 0.50) further supports the presence of hydrocarbons, as it indicates that the pores are not predominantly filled with water. Furthermore, the high hydrocarbon saturation (ranging from 0.50 to 0.60) confirms the significant presence of hydrocarbons within the reservoir. The quantitative assessment of these petrophysical properties across all analyzed wells further confirms the good reservoir quality and potential of the AR/F Member. The integration of seismic and petrophysical data provides a comprehensive understanding of the reservoir characteristics, highlighting the AR/F Member as a significant oil reservoir in the eastern border of the Abu Gharadig Basin, particularly in Heba Field. This study's findings complement the existing knowledge of oil-bearing formations in the region, suggesting that the AR/F Member could be a valuable addition to the basin's hydrocarbon resources. The results underscore the importance of further exploration and development of the AR/F Member to fully realize its potential as a major contributor to the area's oil production.

Pilot Area Formation Evaluation: Upper Shale Member/ Rumaila Oil Field

This study aims to conduct a comprehensive formation evaluation of a pilot area within the Upper Shale Member of the Rumaila Oil Field. This evaluation is an essential step in the full development of the field. The application of well-log data and core analyses can help in obtaining the desired information about the geological characteristics of the formation. The process begins with measuring the formation temperature and water resistance utilizing Schlumberger’s charts and equations. The volume of shale was determined by two different methods, which were then used together to obtain the final shale volume. The porosity was determined using the conventional porosity equations from the porosity logs and the saturation was estimated based on Archie's equation. In core data analysis, an unconventional technique was utilized to determine rock type and permeability. The core porosity and the permeability were classified into four groups mainly using a self-organizing map and an unsupervised machine-learning method, and selected regression equations of each group were applied to estimate permeability in the core. The method depicted a good agreement between the core and estimated permeabilities, proving it as an effective tool. A complicated training data set was constructed based on the use of a multilayer perceptron neural network on coreless wells to identify rock types and permeability. Analyzing the petrophysical properties of the study area showed evidence that this area is characterized by heterogeneity. The heterogeneity of this formation is due to the presence of a considerable amount of shale, in addition to the significant characteristic differences in the layers and the same layer in different locations. The abundance of shale rock poses challenges during drilling operations, particularly due to shale washout which can lead to mechanical issues with the drilling string. Therefore, caution is advised when drilling new wells in the area to mitigate shale washout risks. Furthermore, the analysis identified layers with high hydrocarbon saturation that are viable for production. Conversely, some layers, characterized by shale presence and poor rock quality, are deemed unsuitable for production, and should not be considered as reservoir rock.

GeosPIn: A MATLAB library for geostatistical petrophysical inversion

The petrophysical inversion of seismic data is one of the key components of seismic reservoir characterization. The goal of petrophysical inversion is to estimate the petrophysical properties of reservoir rocks, such as porosity, volumes of minerals or lithologies, and water and hydrocarbon saturations, from seismic data. This process can be performed by combining seismic amplitude-variation-with-offset (AVO) modeling and rock-physics relations with deterministic or probabilistic inverse theory methods, either in a two-step approach based on seismic AVO inversion and rock-physics inverse mapping or in a single-loop step that includes both. We present a comprehensive MATLAB library named Geostatistical Petrophysical Inversion. The novelty of the library lies in a single-loop geostatistical inversion method based on probability field simulation and ensemble smoother multiple data assimilation, as well as its implementation for applications to 3D data sets. The library also includes a Bayesian petrophysical inversion based on the two-step approach for comparison. We illustrate the main algorithms, explain the structure of the source codes, and demonstrate the library applications through 1D and 3D examples.

A sequence decomposition genetic algorithm for petrophysical inversion with spatial correlation

Predicting petrophysical properties based on seismic attributes is essential for accurate subsurface characterization. Typically, a forward model is first established to relate the petrophysical properties to geophysical measurements, and then the petrophysical properties are inverted from the actual measurements. Imposing prior constraints of the spatial relationship is also desirable to regularize the ill-posed inverse problem. However, petrophysical inversion involving spatial correlation presents a high-dimensional optimization problem with multiple local extrema. This problem can be challenging to solve, particularly when discrete variables (such as lithofacies) and continuous variables (such as porosity and fluid saturation) are considered together. Therefore, we develop a global optimization algorithm that uses the Markov chain decomposition and a genetic algorithm for a joint discrete-continuous petrophysical inversion with spatial correlation. The innovation of this algorithm is to spatially decouple the sequence optimization problem into multiple low-dimensional subproblems and overcome the initial value dependence to converge to the global optimal solution. Furthermore, an approximation operation is introduced to expedite the optimization algorithm. Numerical experiments show that after algorithmic acceleration, the spatially correlated inversion requires the same computational time as a simple spatially uncorrelated inversion and achieves significantly better accuracy than the latter. Finally, we apply the method to a case study in East China, wherein the lithofacies, clay content, porosity, and water saturation are inferred from the data of the wave velocities and density. The inversion results around the wells generally match the well-logging data, verifying the feasibility of the new method.

Machine Learning Assisted Prediction of Porosity and Related Properties Using Digital Rock Images.

Accurately estimating reservoir rock properties is paramount for modeling the storage and flow of fluids (hydrocarbon, carbon dioxide, and groundwater) in porous media. However, existing laboratory techniques to measure rock properties are usually time-consuming, expensive, and computationally intensive. This work proposes an efficient workflow that uses the machine learning algorithm, based on the convolutional neural network (CNN) framework, to predict rock properties from microcomputed tomography (micro-CT) X-ray images. The workflow involves data preprocessing, label extraction, training, and prediction using the segmented images of the rock to predict porosity, throat area, and pore surface area, which are essential for pore-scale modeling. The model was trained and validated on the Bentheimer sandstone, which was then used to predict properties of other sandstones (Castlegate and Leopard) with different pore structures and flow properties. The model yielded a good prediction for the throat and pore surface area but a significant error for porosity. Subsequently, a new complex model was trained and validated using diverse images from Bentheimer and an additional rock Castlegate, which was then used to predict the properties of Leopard sandstone. The new model improved the prediction of each property, resulting in mean absolute percentage error (MAPE) values of 2.19%, 3.04%, and 6.08% for porosity, pore surface area, and throat area with the binary images, respectively. In addition, we present a novel data-driven method using a simple regression model to predict the absolute permeability of a digital rock sample using the pore network parameters as predictors. The extreme gradient boost (XGBoost), which performed the best among several machine algorithms, was trained and validated using digital rock images from Bentheimer and Castlegate sandstone. The generated model was then used to predict the absolute permeability of the Leopard sandstone with an R 2 of 0.813, which was a significant improvement over the model generated solely by using either the Bentheimer or the Castlegate sandstone images. Furthermore, our analysis showed that the tortuosity had the most significant effect on the absolute permeability prediction of the rock sample. This study showed that we can reliably predict the morphological properties of porous media using computationally efficient models generated from digital rock images, which can be used to build a regression model to predict the crucial petrophysical properties needed to model the flow of fluids in porous media.

Prędkość ultradźwiękowa analizy wpływu zawartości wody na kredę fosfatyczną

Water content may have a crucial effect on the petrophysical and mechanical properties of high porosity rocks such as chalk. Consequently, the water fluctuations may significantly influence the behaviour of engineering structures that are constructed in chalks. It is the aim of this study to illustrate how the elastic and strength properties of chalk can evolve considering water content variation using P-wave velocity and analytical analyses. To study the influence of water on the ultrasonic compressional wave velocity (VP), measurements of travel time of three samples subjected to different water content were performed. The Uniaxial Compressive Strength (UCS) in dry and saturated chalk specimens was also estimated. To clarify the possible influence of lithology on the physicomechanical properties of rocks, three samples of phosphatic chalk were studied in thin sections. Automated mineral analysis and textural imaging of the samples were performed using an FEI QEMSCAN®. The analyses of the Ultrasonic tests reveal that changes in the water content are associated with variations in the P-wave velocity. Based on P-wave velocity changes as a function of water content, analytical models have been used to predict the elastic and strength properties. The data indicate that the presence of water significantly reduced the elastic and strength parameters of the chalk, a result that is in agreement with the UCS laboratory tests. The derived equations can be used for predicting the elastic and strength properties of high-porous chalk from the P-wave velocity as a function of water content. This approach may avoid the necessity for time-consuming laboratory testing.

Combined Effect of the Microstructure and Mechanical Behavior of Lateritic Soils in the Instability of a Road Cut Slope in Rwanda

A very common hazard in Rwanda is represented by the instability of steep road cut slopes in lateritic soil. In its natural state, this material appears as a fine-grained weak and altered rock, generally in unsaturated conditions. Steep cut slopes made by this material could remain stable for a long time unless weathering weakens its mechanical behavior and heavy rainfall provokes a rapid landslide. This paper presents the results of an experimental investigation on the microstructural, petrophysical, and geotechnical properties of lateritic soil from a road cut slope located in Kabaya (Ngororero District—Rwanda), which was recently subjected to a landslide. The mechanical properties of the material are strictly related to the geological origin and history of the deposits, their formation environment, and weathering processes. These characteristics were revealed by peculiar microstructural features (micro-texture, porosity, and degree of alteration of original mineral paragenesis). The experimental investigations included identification and classification tests, direct shear tests on saturated samples, and swelling tests. This multidisciplinary approach provided insights into the relationship between geotechnical properties and the microstructural, petrophysical, and chemical characteristics of the altered rocks. This study showed how different levels of chemical alteration operated by weathering processes, in conjunction with brittle deformation related to the tectonic history, formed in the same site two shallow rock layers with similar macro-scale features and mechanical behaviors but markedly different microstructural and chemical properties. The innovative aspect of this research suggests an integrated multidisciplinary approach to considering microstructural aspects in addition to mechanical behavior in the slope stability analyses in lateritic soil. In particular, this study demonstrates the importance of such an approach since the failure mechanism is better explained if it is based on microstructural observations instead of considering the soil shear strength parameters only. This research helped to explain the formation of the landslide failure mechanism in a specific road cut slope, which could be assumed as representative of many other similar slopes subjected to landslides in Rwanda.

Heterogeneous Reservoir Petrophysical Property and Controlling Factors in Semi-Restricted Depositional Setting: A Case Study of Yamama Formation, X Oilfield, Middle East

The Early Cretaceous Yamama Formation of X oilfield, deposited in a semi-restricted setting, holds considerable oil reserves. However, the reservoir is extremely heterogeneous and is poorly studied. Integrating outcrops, cores, cast thin sections, regular or special core analysis, wireline logging data from six wells, and seismic data, this study provides an improved understanding of reservoir petrophysical characteristics and geological controlling factors including sedimentation, diagenesis, and sequence. The results showed that eight lithologies are developed in the Yamama Formation, of which packstone and wackstone are dominant. The physical properties span a wide range, with porosity mainly distributed between 10% and 25%, and the permeability mainly distributed between 0.1 mD and 1 mD. Nine types of pores are developed, with moldic pores, micropores, and skeletal pores being the most developed. The reservoir has six types of microstructures, of which the poorly sorted with mega-throat represent the best reservoir. The Yamama Formation was mainly deposited in a lagoon, along with five other facies, such as supratidal flat, patchy reef, back shoal, shoal, and open shelf. Six types of diageneses are developed, with dissolution during the penecontemporaneous stage being the most beneficial to the reservoir and cementation being the most destructive. Three sequences were recognized in the Yamama Formation. It concluded that the hydrodynamics in semi-restricted depositional setting is weak overall and does not have the potential to develop large-scale high-quality reservoirs. A wide range of bioclasts were selectively dissolved to form a large number of secondary pores. Sediments rich in Algae, Bacinella, and peloids tend to form moldic pores, skeletal pores, and intergranular pores, respectively, which are prone to be favorable reservoirs. Controlled by the coupling of sedimentation and diagenesis driven by sequence, the reservoir is extremely heterogenous.

Machine learning assisted Kriging to capture spatial variability in petrophysical property modelling

Machine learning (ML) models have been widely used for estimating formation geophysical properties. While ML models excel at capturing non-linear correlations among parameters, incorporating the impact of spatial variability into the training and testing process remains challenging. On the other hand, deterministic geostatistical models such as Kriging are capable of capturing spatial variability to a great extent but often rely on the assumption of linearity, making them less suitable for non-linear relations. In this study, a machine learning (Least Squared Support Vector Regression, LSSVR) model is integrated into Kriging interpolation to estimate the sonic compressional wave velocity with high spatial variability over a geological setting. This new methodology consists of two steps: (1) constructing an initial machine learning model using LSSVR and calculating the residuals, and (2) applying Kriging interpolation and gradually modifying the training data using Kriging estimation to optimize the machine learning model. The proposed model effectively captures the non-linear relation between auxiliary and target variables along with the spatial autocorrelation of data. To evaluate the performance of proposed hybrid model, a dataset comprising geophysical downhole logging measurements including density, gamma ray, depth and sonic velocity were extracted from 122 boreholes in a mine site located at the Bowen basin in Queensland, Australia. The data was averaged over a sandstone interval and divided into training and testing sets. Four models, namely linear regression, regression Kriging, LSSVR and the hybrid model were developed and compared with each other based on their R2 and RMSE values. The results demonstrate that the hybrid model outperforms the other models highlighting the effectiveness of employing a hybrid approach in capturing the spatial variability in petrophysical data over geological formations.

Shear-wave velocity prediction of tight reservoirs based on poroelasticity theory: A comparative study of deep neural network and rock physics model

The absence of shear-wave (S-wave) velocity in well log data limits the effective seismic characterization of lithology, petrophysical properties, and fluid distribution of tight reservoirs in the Chang 7 shale oil formations of Ordos Basin, west China. However, conventional rock physics models (RPMs), e.g., the Xu-White model, struggle to accurately obtain the key parameters such as pore aspect ratio in the process of S-wave velocity prediction. This work performs a comparative study of S-wave velocity prediction based on deep neural network (DNN) and RPM for such reservoirs. By employing the poroelasticity equations which are capable of describing elastic wave propagation in the complex reservoirs saturated with viscous fluid, the relation between elastic and petrophysical properties is established with a DNN-based method. This approach quantifies the connection between P-/S-wave velocities and rock properties. On the other hand, we reformulate the traditional Xu-White model by incorporating the decoupled Kuster-Toksöz (K-T) model and the poroelasticity equations with viscoelastic fluid in the modeling process. The variation of pore aspect ratio with depth is also considered to characterize the complex pore structures, which is appropriate for improving the accuracy of prediction. Log data from the three wells are considered for verification, of which results show that the S-wave velocity prediction methods help improve the final result. Aided by the DNN-based method, the prediction exhibits a better performance if the training data is sufficient. In particular, provided a small amount of measured S-wave data (even ten data points), the trained DNN model can also provide reasonable predictions.

The interplay of detrital modes and depositional facies on diagenesis and petrophysics of deep-marine Miocene sandstones (Cilento Group), Southern Apennines, Italy

The Cilento Group (Langhian-to-Tortonian) is a thick turbiditic succession of the Southern Apennines foreland region that unconformably overlain the Lucanian oceanic terranes. The San Mauro Formation (SMF) form the uppermost portions of the Cilento Group, and consists of 1400–1600 m thick turbiditic succession including quartzolithic, volcanolithic, and quartzofeldspathic sandstones with several carbonatoclastic layers interbedded, passing upward from distal-to proximal-facies associations. Here, based on the vertical changes of texture and petrology in the succession, the SMF was divided in two main sections. The key depositional features and the main post-depositional processes, of lower- and upper-part of SMF, are compared with the most significant petrophysical properties with the purpose to elucidate the major controls on the porosity and permeability evolution during burial. With this aim, fifty-four thin sections were petrographically investigated, while mercury intrusion analysis was performed on twenty-four samples. Compaction, cementation, dissolution and fracturing occurred in the SMF, altering porosity and permeability. The main authigenic minerals are: 1) carbonates, mainly calcite and less dolomite, dominantly occurring as replacement of framework grains; 2) Phyllosilicates, mainly developing in the upper part of SMF as pore-filling cement or as small and incomplete grain coatings; 3) Fe-oxides occurring as coatings or localized crystals. The relationship between the compactional porosity loss (COPL) and the cementational porosity loss (CEPL) testifies the primary role of compaction in decreasing porosity, reducing the intergranular volume (IGV) to 11.1% in the lower SMF and to 16.7 % in the upper SMF. The coarse upper SMF samples exhibit lower mean porosity and permeability (6 % and 215 mD, respectively) than the fine lower SMF samples (9.2 % and 774.2 mD, respectively). In the upper section of SMF, a comparatively more widespread pore-filling cementation and the abundant detrital matrix affect depositional intergranular porosity, reducing the pore size and interconnectivity. Moreover, the higher amount of rigid grains (petrofacies dependent) and their brittle deformation produce an intricate fragmentation and micro-fracturing system, altering pores geometry and affecting permeability. On the contrary in the lower SMF, ductile components suffer compaction more than rigid ones, leading the lowest IGV. In addition, a higher volume of authigenic carbonates occur as replacement on framework components, hence playing a minor occluding role and less affecting the pore system. Composition and tectonics rule the effects of diagenetic processes in the San Mauro turbiditic succession, following the vertical evolution of depositional facies association and petrology.

Sedimentary facies control on fracture and mechanical stratigraphy in siliciclastics: Marnoso-arenacea formation, Northern Apennines, Italy

Jointing in bedded siliciclastic sedimentary successions has been mainly analyzed in terms of fractured layers thickness-normalized joint spacing and related probability distributions, commonly neglecting the influence of primary structures on the petrophysical properties, joint height patterns, spatial arrangements and best-fitted spacing distributions. Taking into account the depositional controls on mechanical and fracture stratigraphy is crucial for improving the predictive potential of geological modelling in exploration and exploitation of underground resources, gas injection and storage. Here, we present a high-resolution, structural-stratigraphic study carried out on a 72 m-thick succession of the Marnoso-arenacea foredeep turbidites, exposed in the Northern Apennines of Italy. The systematic integration of the structural and sedimentological characterization of this basin-plain turbiditic succession, coupled with petrophysical and geomechanical measurements, represents the fracture and mechanical stratigraphy database onto which we tested the standardly used fracture density parameters and the best-fitted probability distributions, i.e. normal, log-normal, gamma and exponential, applied to fracture spacing analysis. Our results document that cumulative analysis does not identify the variability of joint height patterns and spacing distributions in siliciclastic turbidites, which instead requires considering single sedimentary facies types. Specifically, sandstones are affected by facies bounded joints and gamma spacing distributions while clay-rich and poorly sorted facies show a higher abundance of top bounded and throughgoing joints, and are best fitted by log-normal spacing distributions. We suggest that the fracture and mechanical stratigraphy should be studied at the facies scale resolution to unravel the strong control of the sedimentary facies for improving the characterization of siliciclastic reservoirs.

Evaluation of Recoverable Hydrocarbon Reserves and Area Selection Methods for In Situ Conversion of Shale

It is well known that the existing horizontal-well-drilling and hydraulic fracturing technology used to achieve large-scale, cost-effective production from immature to low–moderate-maturity continental shale in China, where the organic matter mainly exists in solid form, is fairly ineffective. To overcome the obstacles, in situ conversion technology seems feasible, while implementing it in the target layer along with estimating the amount of expected recoverable hydrocarbon in such shale formations seems difficult. This is because there are no guidelines for choosing the most appropriate method and selecting relevant key parameters for this purpose. Hence, based on thermal simulation experiments during the in situ conversion of crude oil from the Triassic Chang 73 Formation in the Ordos Basin and the Cretaceous Nenjiang Formation in the Songliao Basin, this deficiency in knowledge was addressed. First, relationships between the in situ-converted total organic carbon (TOC) content and the vitrinite reflectance (Ro) of the shales and between the residual oil volume and the hydrocarbon yield were established. Second, the yields of residual oil and in situ-converted hydrocarbon were measured, revealing their sensitivity to fluid pressure and crude oil density. In addition, a model was proposed to estimate the amount of in situ-converted hydrocarbon based on TOC, hydrocarbon generation potential, Ro, residual oil volume, fluid pressure, and crude oil density. Finally, a method was established to determine key parameters of the final hydrocarbon yield from immature to low–moderate-maturity organic material during in situ conversion in shales. Following the procedure outlined in this paper, the estimated recoverable in situ-converted oil in the shales of the Nenjiang Formation in the Songliao Basin was estimated to be approximately 292 × 108 tons, along with 18.5 × 1012 cubic meters of natural gas, in an area of approximately 8 × 104 square kilometers. Collectively, the method developed in this study is independent of the organic matter type and other geological and/or petrophysical properties of the formation and can be applied to other areas globally where there are no available in situ conversion thermal simulation experimental data.

Rock Mineral Volume Inversion Using Statistical and Machine Learning Algorithms for Enhanced Geothermal Systems in Upper Rhine Graben, Eastern France

AbstractAccurately determining the mineralogical composition of rocks is essential for precise assessments of key petrophysical properties like effective porosity, water saturation, clay volume, and permeability. Mineral volume inversion is particularly critical in geological contexts characterized by heterogeneity, such as in the Upper Rhine Graben (URG), where both carbonate and siliciclastic formations are prevalent. The estimation of mineral volumes poses challenges that involve both linear and nonlinear relationships associated with geophysical data. To address this complexity, our methodology strategically integrates the robust insights from standard statistical approaches with three machine learning (ML) algorithms: multi‐layer perceptron, random forest regression, and gradient boosting regression. Furthermore, we propose a new hybrid ensemble model that incorporates a weighted average of multiple ML approaches to predict mineral composition within the Muschelkalk and Buntsandstein formations of the URG. ML techniques for mineral composition prediction in these formations exhibit robust predictive performance. The predicted mineral volumes align closely with quantitative estimates derived from X‐ray diffraction analysis. Additionally, they are in good qualitative agreement with mineral descriptions obtained from cores and cuttings of the Muschelkalk and Buntsandstein formations.

Investigation of petrophysical properties of synthetic carbonate plugs: Adding a novel 3D printing approach to control pore networks

The physical and chemical properties of rocks play a crucial role in understanding fluid-solid flow behavior at the pore level. Thus, studying pore space characteristics is important in evaluating and determining petrophysical properties of various rock types, including synthetic rocks, which can be designed to mimic natural rocks. This study investigates the petrophysical properties of synthetic carbonate plugs by using a new approach that correlates the base material and a 3D printing solution with porosity and permeability. The research shows that with precise particle size and morphology, pure mineral materials such as calcite, dolomite, quartz and a non-structural Portland cement may produce a controlled rock matrix. The synthetic plugs developed in this study exhibit controlled macro, meso, and micro porosities, including fractures and vuggys, by the solubilization of materials with controlled morphology by 3D printing, which provides valuable information on regulating pore space in synthetic carbonate rocks.