Petrophysical Characteristics Research Articles

Analysis of Reservoir Properties and Quantification of Hydrocarbon Volumes Within LK Field, Niger Delta, Nigeria

This work analyzes and estimates the qualities and volumes of hydrocarbons in the identified reservoirs (Thin Sand and Sand B) of the LK field in the Niger Delta, Nigeria. The LK field is a promising hydrocarbon reservoir located at the border of the Greater Ughelli Depobelt in the Niger Delta Basin. The data utilized encompassed a 3D seismic survey, which was analyzed to delineate faults, identify stratigraphic horizons, and generate maps illustrating the time and depth structures within the subsurface. Well log data, including gamma ray, resistivity, and density/neutron measurements, were analyzed to evaluate and describe the petrophysical characteristics of the subsurface rock formations. The Thin Sand reservoir unit contains natural gas, while the Sand B reservoir is divided into two separate zones: the first zone (Z1) contains natural gas, and the second zone (Z2) contains oil. For Sand B, Z1, and Z2, the gross thicknesses are 13.16m and 57.46m, net thicknesses are 11.32m and 45.39m, NTGs are 0.86 and 0.79, shale volumes (Vsh) are 0.13 and 0.21, effective porosities (?e) are 0.28 and 0.21, water saturations (Sw) are 0.11 and 0.39, and hydrocarbon saturations (Sh) are 0.89 and 0.61, respectively. For the Thin Sand reservoir, the gross thickness is 6.00m, net thickness is 4.74m, NTG is 0.79, Vsh is 0.21, ?e is 0.23, Sw is 0.12, and Sh is 0.88. The stock tank oil initially in place (STOIIP) in the Sand B, Z2 reservoir is estimated to be 46.8 million barrels, with a recoverable oil volume of 9.4 million barrels, assuming a 20% recovery factor. The stock tank gas initially in place (STGIIP) is estimated at 10.4 billion cubic feet (Bcf) in the Thin Sand reservoir and 30.6 Bcf in the Sand B, Z1 reservoir, with recoverable gas volumes projected at 8.3 Bcf and 24.5 Bcf, respectively, assuming 80% recovery factors. After estimating the volume of hydrocarbons in the known reservoirs, significant and economic amounts of gas and oil for commercial purposes were discovered. However, this research study faces challenges such as uncertainty, unreliability, and varying scales due to different origins within the field. Further research using unconventional computational tools together with high-resolution simulating software is needed to produce more detailed and accurate results.

A comprehensive study on optimizing reservoir potential: Advanced geophysical log analysis of zamzama gas field, southern indus basin, Pakistan

This study investigates the hydrocarbon and gas potential of the Pab Formation's Late Cretaceous reservoir in the Zamzama Gas Field (ZGF) situated in Pakistan's Southern Indus Basin (SIB). Vital insights to support Pakistan's escalating energy requirements. Petrophysical characteristics of the Pab Formation (PFm) zones of interest in the ZGF were determined by evaluating geophysical logs from four wells. Various techniques were employed to analyze the reservoir rock type (RRT), lithofacies, and spatial distribution of petrophysical parameters. The petrophysical assessment reveals that gas-bearing zones primarily consist of sandstone with intermittent shale layers. These reservoirs exhibit distinguishing attributes, including clean sand composition, high resistivity, total porosity (∅T), good permeability (K), minimal shale content (Vsh), and low water saturation (Sw). The total porosity ranges from 12% to 16%, permeability from 14 mD to 19 mD, water saturation levels between 34% and 44%, and hydrocarbon saturation (Sh) from 56% to 66%. The findings suggest significant gas reserves within the studied sand units of the Zamzama Block. Overall, this research provides a detailed and comprehensive understanding of reservoir characteristics, thereby increasing the potential for further hydrocarbon exploration and production in the study area.

Geo-mechanical and geo-morphology characterisation of the cap rocks in the Niger Delta for potential carbon capture and storage

The world has been battling climate change which is caused by an excessive amount of carbon dioxide in the atmosphere. Carbon sequestration in geological sinks was identified as one of the major methods to curb and reduce the amount of carbon dioxide in the atmosphere. The carbon dioxide stored in the geological formations must not escape back into the atmosphere. Characterisation of potential reservoirs for geological sequestration is pertinent for injectivity, capacity, and most importantly containment of carbon dioxide. The cap rock is one of the most important factors determining carbon sequestration success. This study focuses on the preliminary characterisation of the caprocks for evaluating potential subsurface storage of carbon dioxide based on their petrophysical characteristics. X-ray Diffraction and X-ray Fluorescence provided the mineralogical composition and geochemical data of the cap rocks while Scanning Electron Microscopy-EDS was used to identify the qualitative information on the micromorphological textural structure of the cap rocks in the Niger Delta. Conventional Triaxial experiments were used to determine the elastic and geo-mechanical strength of the caprocks. It was identified that the grain skeleton of caprocks in the region is predominated by Quartz, Albite, and Muscovite. The ratio of the tectosilicate and phyllosilicate minerals indicates that the cap rocks are silicate shale. The peak strength of the caprocks ranged from 48.85 to 80.50 MPa and are classified as strong rocks. The cap rocks showed a quasi-elastic behaviour after being subjected to compressive axial force. The elastic modulus of the caprocks was also observed. The characteristics exhibited by the shale caprock at the rock matrix level are highly favourable for sealing carbon dioxide and hence indicate capability for use in carbon sequestration.

Integrated analysis of wireline logs analysis, seismic interpretation, and machine learning for reservoir characterisation: Insights from the late Eocene McKee Formation, onshore Taranaki Basin, New Zealand

This study aims to integrate seismic and well data for re-assessment and delineating the geological and petrophysical characteristics of the Late Eocene McKee Formation. Despite the McKee Formation being a hydrocarbon-producing field, there have been no previous integrated studies conducted to characterize its deposits in the Taranaki Basin. To execute these tasks, the process involves the interpretation of seismic data, wireline analysis, and 3D porosity volume prediction using multi-layered feed-forward neural network models. The seismic data was interpreted and found to contain low displacement faults due to intricate geometric complexities, the contour map that was generated shows lateral extension and thickness variation, and the region of direct hydrocarbon indicators was detected using the root-mean-square. Additionally, the seismic inversion provides a comprehensive understanding of the acoustic impedance variations from low to high value, which signifies the presence of mudstone clasts content. Furthermore, in the wireline log analysis, the reservoir was found to be a Silici-clastic and carbonate field owing to the presence of dolomite, calcite, and quartz. After applying cutoffs, petrophysical parameters were evaluated. The estimated values for effective porosity, reservoir thickness, shale volume, and water and hydrocarbon saturation were 15.04 % to 17.29 %, 85.44 m to 122.41 m, 8.99 to 14.87 % and 12.97–27.10 %, and 72.9 % to 87.03 %, respectively. The outcome of this extensive research aids in accurately characterizing this region for the future prospectively of the field. This integrated study can be applied and adapted to other basins in New Zealand and the southwestern Pacific. The proposed workflow is also suitable for analogous scenarios in various basins worldwide.

A novel deep learning method based on 2-D CNNs and GRUs for permeability prediction of tight sandstone

The accurate calculation of tight sandstone reservoir permeability is crucial for optimizing production and maximizing natural gas recovery. Traditional physical model-based permeability calculation methods heavily rely on core experimental parameters. However, petrophysical experiments are costly, prompting the development of fast and reliable reservoir permeability prediction methods. In this paper, a novel deep learning model that combines two-dimensional convolutional neural network (2-D CNN) model and gated recurrent neural network is proposed to predict tight sandstone reservoir permeability. The 1-D conventional well logs data is converted into the 2-D geological feature map to fit the model's input dimension. The TimeDistributed layer wrapping technology is used to couple the 2-D CNN and GRU to extract comprehensive features of geological feature maps. By training the model, a depth nonlinear mapping between the spatial and temporal features in the well logs data and permeability is established. The model is divided into two subnetworks, 2-D CNNs and GRUs, which are used to compare and analyze the functions of different modules in the proposed model. Additionally, two traditional machine learning algorithms, SVM and XGBoost, are also introduced as comparison models. Four regression evaluation metrics are used to quantify the predictive performance of different models. The test results in a blind well demonstrate that the proposed method outperforms the comparison models in predicting permeability, with a coefficient of determination (R2) of 0.9305, a root mean square error (RMSE) of 0.0895, a mean squared error (MSE) of 0.0080, and a mean absolute error (MAE) of 0.0725. This model can provide insight into the petrophysical characteristics of reaction reservoirs based on conventional well logs data and can accurately estimate reservoir permeability under limited core data, which has great application potential in improving the exploration accuracy of unconventional oil and gas resources.

Retraction Note: Petrophysical characterization and durability aspects of tufa rocks at Kurkur Oasis, Western Desert, Egypt

Retraction Note: Petrophysical characterization and durability aspects of tufa rocks at Kurkur Oasis, Western Desert, Egypt

Field tests of geological storage of CO 2 at the Otway International Test Centre, Australia: trapping and monitoring the migrating plumes

The capture and geological storage of CO 2 is essential for net zero. Large volumes of secure subsurface storage will be required, much in unexplored saline aquifers. At the Otway International Test Centre we have executed a series of storage tests, including geophysical surveys, geochemical and petrophysical characterization, drilling and instrumenting six deep wells and conducting two intensively monitored injections each of 15 Kt CO 2 . We summarize the execution and outcomes to draw out the implications for larger-scale storage and for migration trapping of CO 2 plumes. Immobilization of most of the plume is essential for long-term safety. Suitable saline aquifers should have sealing caprocks but need not have structural traps. Buoyant CO 2 could move kilometres beneath the seal, with immobilization occurring by capillary forces and dissolution. Predicting and monitoring plume behaviour with practical methods integrated with risk management will be necessary. We characterized and monitored migration trapping, demonstrated plume immobilization, and tested monitoring methods tailored to large saline aquifers. We developed pressure tomography and continuous seismic monitoring; these provide less detailed information than conventional methods but can be targeted at risks. Passive methods, such as the seismic response to earthquakes, can also act as sentinels for specific events. These methods can bridge the ‘monitoring gap’ between detailed but infrequent methods and more frequent but simpler methods and the results will enable better trade-offs between cost, complexity and risk for a technology crucial for controlling CO 2 levels in the atmosphere. Supplementary material : Detailed descriptions of unpublished modelling are available at https://doi.org/10.6084/m9.figshare.c.7148197

Reservoir evaluation of dolomitized Devonian strata in the Western Canada Sedimentary Basin: Implications for carbon capture, utilization and storage

Differentially dolomitized carbonate strata in the Western Canada Sedimentary Basin (WCSB) are increasingly targeted for carbon capture, utilization and storage (CCUS), yet few studies have evaluated the petrophysical characteristics of these conventional hydrocarbon reservoirs for this purpose. To address this, this study uses drill core analysis (sedimentology, diagenesis, pore morphology and distribution), together with core plug and production data, to evaluate the properties of five depleted oil and gas fields in the Middle to Upper Devonian Swan Hills Formation, Leduc Formation and Wabamun Group. The Swan Hills and Leduc Formations are comprised of reef, shoal and lagoon deposits that are predominantly fossil-rich (e.g. stromatoporoid-dominated rudstones and boundstones). In contrast, the carbonate ramp deposits of the Wabamun Group are fossil-poor, consisting instead of variably bioturbated carbonate mudstones, wackestones and packstones. Replacement dolomitization is variable throughout each stratigraphic unit, but generally occurs within fossil-rich and/or heavily bioturbated intervals. Fracture densities are broadly comparable in limestone and dolostone. Porosity in the Swan Hills and Leduc Formations is predominantly moldic and vuggy, occurring where fossils (e.g. stromatoporoids) are partially or fully dissolved. Pore space in the Wabamun Group is mostly restricted to intercrystalline porosity within burrows. In general, burial cements (e.g. calcite and dolomite) are volumetrically insignificant and only partially fill pores. Exceptions to this include porosity-occluding cements associated w ith fractures and breccias in the vicinity of faults. Dolomitization and depositional facies are found to exert a strong control on pore morphology, distribution and interconnectivity. Porosity is primarily controlled by the relative abundance of skeletal grains and by the presence of burrows. These highly porous facies acted as fluid pathways during burial diagenesis, resulting in their preferential dolomitization, solution enhancement of pre-existing pores, and creation of volume reduction-related porosity. The high CO2 storage capacity and low unplanned plume migration risk (due to depositional/diagenetic baffles) of dolomitized reefal reservoirs (e.g. Swan Hills and Leduc Formations) make them more attractive targets for CCUS than those with limited capacity and/or potential migration pathways (e.g. fault-related fractures and breccias in the Wabamun Group). These results demonstrate that drill core analysis, in combination with legacy data, can provide valuable insights into the factors that control reservoir CO2 injectivity, plume migration and storage capacity.

Improving permeability prediction via Machine Learning in a heterogeneous carbonate reservoir: application to Middle Miocene Nullipore, Ras Fanar field, Gulf of Suez, Egypt

Predicting and interpolating the permeability between wells to obtain the 3D distribution is a challenging mission in reservoir simulation. The high degree of heterogeneity and diagenesis in the Nullipore carbonate reservoir provide a significant obstacle to accurate prediction. Moreover, intricate relationships between core and well logging data exist in the reservoir. This study presents a novel approach based on Machine Learning (ML) to overcome such difficulties and build a robust permeability predictive model. The main objective of this study is to develop an ML-based permeability prediction approach to predict permeability logs and populate the predicted logs to obtain the 3D permeability distribution of the reservoir. The methodology involves grouping the reservoir cored intervals into flow units (FUs), each of which has distinct petrophysical characteristics. The probability density function is used to investigate the relationships between the well logs and FUs to select high-weighted input features for reliable model prediction. Five ML algorithms, including Linear Regression (LR), Polynomial Regression (PR), Support Vector Regression (SVR), Decision Trees (DeT), and Random Forests (RF), have been implemented to integrate the core permeability with the influential well logs to predict permeability. The dataset is randomly split into training and testing sets to evaluate the performance of the developed models. The models’ hyperparameters were tuned to improve the model’s prediction performance. To predict permeability logs, two key wells containing the whole reservoir FUs are used to train the most accurate ML model, and other wells to test the performance. Results indicate that the RF model outperforms all other ML models and offers the most accurate results, where the adjusted coefficient of determination (R2adj) between the predicted permeability and core permeability is 0.87 for the training set and 0.82 for the testing set, mean absolute error and mean squared error (MSE) are 0.32 and 0.19, respectively, for both sets. It was observed that the RF model exhibits high prediction performance when it is trained on wells containing the whole reservoir FUs. This approach aids in detecting patterns between the well logs and permeability along the profile of wells and capturing the wide permeability distribution of the reservoir. Ultimately, the predicted permeability logs were populated via the Gaussian Random Function Simulation geostatistical method to build a 3D permeability distribution for the reservoir. The study outcomes will aid users of ML to make informed choices on the appropriate ML algorithms to use in carbonate reservoir characterization for more accurate permeability predictions and better decision-making with limited available data.

Bayesian NMR petrophysical characterization

Identification of reservoir rock types is necessary for the exploration and recovery of oil and gas. It involves determining the petrophysical properties of rocks such as porosity and permeability which play a significant role in developing reservoir models, estimating the volumes of oil and gas reserves, and planning production methods. Nuclear magnetic resonance (NMR) technology is a fast and accurate tool for petrophysical rock characterization. The distributions of relaxation times (T2 distributions) offer valuable insights into the distribution of pore sizes in rocks, and these distributions are closely linked to important petrophysical parameters like porosity, permeability, and bound fluid volume (BFV).This work introduces a Bayesian estimation method for analyzing NMR data. The Bayesian approach uses prior knowledge of T2 distributions in the form of the prior mean and covariance. The Bayesian approach combines prior knowledge with observed data to obtain improved estimation. We use the Bayesian estimation method where prior information regarding the rock sample type, for example shale, is available.The estimators were evaluated on decay data simulated from synthesized distributions that replicate the features of experimental T2 distributions of three types of reservoir rocks. We compared the performance of the Bayesian method with two existing methods using porosity, bound fluid volume (BFV) geometric mean (T2LM) and root mean square error (RMSE) of the estimated T2 distribution as evaluation criteria. Additional experiments were carried out using experimental T2 distributions to validate the results.The performance of the Bayesian methods was also tested using mismatched priors.The experimental results illustrate that the Bayesian estimator outperforms other estimators in estimating the T2 distribution. The Bayesian method also outperforms the ILT method in estimating derived petrophysical properties except in cases where the noise level is below 0.1 and the T2 distributions are associated with short relaxation times.

3D geological and petrophysical modeling of Alam El-Bueib Formation using well logs and seismic data in Matruh Field, northwestern Egypt

There are several productive petroleum fields in the North Western Desert (WD) of Egypt, which received extensive investigations regarding their petroleum potential. However, a few studies tackled the Matruh Oil Field, which contains the oil prolific Early Cretaceous Alam El-Bueib Formation (AEB Fm) reservoir. The reservoir intervals of the AEB Fm show substantial lithological variations across the basin. Therefore, it is necessary to analyze the vertical and lateral distributions in terms of their lithological and petrophysical properties. To achieve this objective, wireline logs of four wells and 20-2D seismic lines were used to construct a depth-structure contour map for the studied part of the field. This map was used to build the field’s structure model and to identify the fault patterns in the basin through several seismic lines. Analyses of well logs data and lithology were used to estimate the petrophysical properties of AEB sandstone units AEB-1, AEB-3A, AEB-3C, and AEB-6. Results show that the AEB-6 Unit is the most promising hydrocarbon-bearing unit. It has a net pay of 20–160 feet, a shale volume of 5–20%, an effective porosity of 14–20%, and a hydrocarbon saturation of 70–88%. The structure-depth maps indicate a number of normal faults with two principal NE-SW and NW–SE trends, which probably act as structural traps in the Matruh Oil Field. The constructed structure-depth maps and calculated petrophysical parameters were used to build a three-dimensional reservoir model. A blind well was used to validate the accuracy and reliability of the facies, porosity, and saturation models for the AEB Fm units, ensuring a good match between log-derived data and built models. The AEB Fm shows regional heterogeneous variations in its petrophysical characteristics. It exhibits unconventional reservoir characteristics in a N–S direction and conventional reservoir characteristics in an E–W direction. This observed heterogeneity shows the need to carry out further investigations to comprehensively assess the hydrocarbon potential of AEB Fm in different areas of the Matruh Basin.

Reservoir characteristics of the middle Eocene Avanah Formation in Erbil governorate, northern Iraq: Integration of outcrop and subsurface data

The current work investigate the petrophysical characteristics of the Middle Eocene Avanah Formation in the Erbil Governorate using IP software to analyze the well data, integrated with the 
 petrographic investigation of the formation in the nearest outcrop in the Gomaspan section. Well logging data revealed that the main lithology of the formation is limestone and dolostone while the lithology in the Gomaspan section is composed of limestone, dolomitic limestone, marly dolomitic limestone, and thin beds of shale. The lower dolomitic unit in the subsurface section of the formation is believed to be the most suitable reservoir unit due to good petrophysical characteristics including, low water saturation, high porous medium, and the presence of movable hydrocarbon. To measure porosities, a variety of well logging techniques were used in this study. The investigated formation was divided into Avanah dense (limestone unit) and Avanah porous (dolostone unit) based on their porous components. The petrographic study shows that most pore types of the formation are secondary and represented mainly by vuggy, moldic, intercrystalline, and fracture types. Most of the fractures in the upper limestone units are filled by calcite cement. The study claimed that the lower part of the formation (dolostone unit) in both sections is considered a good reservoir.

Fabric and Fluid Inclusions Characterization of a Stalagmite from Eastern Spain: A Precondition for Noble Gas Analysis by Step-Crushing Methodology

Fluid inclusions in stalagmites are becoming increasingly important for paleoclimate research. Within this framework, noble gas thermometry, based on noble gases dissolved in water from fluid inclusions, provides quantitative estimations of cave air paleotemperature. Two major issues of Noble Gas Temperature (NGT) determination on speleothems are (1) the potential lack of enough water for the analysis and (2) the presence of trapped gas not dissolved in water that can be released during the analysis from biphasic or all-gas fluid inclusions, as its contribution to the bulk noble gas signal can hinder NGT results. Although the step-crushing method helps to reduce the second issue, it also decreases the amount of water available for the calculations. In order to obtain reliable NGT results with low uncertainties, a major challenge is still to reach a balance between sufficient water for analysis and a small amount of “atmospheric” gas. The difficulty is that the extraction process cannot be standardized since it strongly depends on the type of sample. The objective of this work is to investigate how the characteristics of the speleothem can determine the adequacy of the extraction process. For this purpose, we consider a stalagmite from a Mediterranean cave that consists of columnar elongated calcite and contains a significant quantity of fluid inclusions, which suggests good potential for NGT analysis. Results, however, were poorly satisfactory. Trying to understand the source of the problems, an integrated study of petrography and petrophysical features was performed. The samples were found to be different depending on the stage of coalescence of crystals and thus separated into “open” and “closed” fabrics. Classic petrographic analysis and non-destructive (nuclear magnetic resonance) techniques were used to characterize the type and amount of fluid inclusions present in both types of fabrics. The study indicates that the closed fabric (total coalescence of calcite crystals) has most water trapped in water-filled, small intracrystalline fluid inclusions that usually contain very little gas. This fabric is very suitable for NGT determination, but since the amount of water is quite small, the sample should be crushed in only one step with a large number of beats to break all the inclusions. In contrast, samples with open fabric (partial coalescence of calcite crystals) contain a higher amount of water and, also, gas-filled large intercrystalline fluid inclusions. For this fabric, step-crushing of the sample is necessary. However, the low amount of water left for the second and third crushings could lead to flawed NGT results. Thus, we suggest modifying the method to get rid of part of the gas in the first crushing while leaving enough water for the following steps. This work shows the importance of characterizing speleothems and fluid inclusions, including their petrography and petrophysical characteristics, before starting NGT analysis, allowing the selection of the most favorable samples and the customization of the step-crushing procedure.

The Effect of Fracturing Fluids Types on Reservoir Fracture Geometry, Production, and Net Present Value

The Effect of Fracturing Fluids Types on Reservoir Fracture Geometry, Production, and Net Present Value

Quantitative Interpretation of the Petrophysical Properties of Selected Wells for the Mishrif Formation in Nasiriyah Oilfield, Southern Part of Iraq

One of the oil field's main reservoirs is the Mishrif Formation, which is found in the Nasiriyah oilfield in the southern part of Iraq (Late Cenomanian-Early Turonian). It was one of the three oil wells selected for this study. To extract various petrophysical parameters for open wells indicated by gamma ray, density, neutron, self-potential, acoustic, and resistance, a variety of well logs were analyzed. The qualitative interpretation of the logs allowed for the identification of different types of rocks, the boundaries and thicknesses of the strata, the depths of the formation, and the zones that contained water and hydrocarbons. The quantitative interpretation, which assesses the reservoir's attributes by computing its porosity, the quantity and distribution of the shales, the levels of water and oil saturation, and other elements, that are necessary to evaluate the reservoir’s units in the research wells. The features of the study wells and the used logs were reported, to make clear how these features were distributed among the sample wells. The collected petrophysical characteristics were handled and shown as charts. There are two types of units within the Mishrif Formation: reservoir-containing CR-I, MA, CR-II, and MB. In most wells, reservoir units are made up of hard, low-porous rocks that are positioned between highly porous reservoir units. For a few wells, the MA unit had low residual and mobile hydrocarbon percentages, whereas the MB unit had large percentages, while the hard rocks had significant percentages of water saturation.

Sensitivity Testing of Marine Turbidite Age Estimates along the Cascadia Subduction Zone

ABSTRACT Marine turbidite studies infer that 19–20 ∼Mw 9 earthquakes ruptured the full Cascadia subduction zone (CSZ) in the past 10 kyr, a hypothesis that relies on concurrent turbidite deposition generated from seismogenic strong ground motion along the ∼1100 km margin. Correlation of marine turbidite deposits is based on petrophysical characteristics and radiocarbon geochronology, the latter of which relies on a series of age corrections and calibrations for marine radiocarbon age and sedimentological parameters. In this work, I isolate several key variables in turbidite age assessment and systematically test how previous assumptions and new calibration curves affect estimated ages, and thus whether geochronologic analyses independently support coeval turbidite deposition. For radiocarbon age calibration, I test the impact of (1) updating global marine reservoir age corrections; (2) updating local marine reservoir age estimates; and (3) selectively applied marine reservoir age excursions. From the calibrated radiocarbon ages, I calculate turbidite age and uncertainty using a Monte Carlo approach with a broad range of sedimentation rates and substratal erosion. By simply updating the global marine radiocarbon calibration, individual radiocarbon ages differ from published estimates by several hundred years. Updates to the local reservoir age corrections are minimal because existing data remain limited yet have potential for great impact on turbidite ages. Of the sedimentological parameters tested, sedimentation rate has the largest impact on estimated turbidite age, with individual ages changing up to 500 yr from published estimates. For radiocarbon samples of turbidites previously inferred to correlate, the individual ages typically show increased scatter and overall uncertainty, even for models that only update the global marine reservoir calibration. These results highlight the major age uncertainty associated with current coseismic turbidite age analyses in Cascadia and how independent constraints on local reservoir corrections and sedimentation rate are critical for accurate turbidite age estimates in the Pacific Northwest.

Utilizing machine learning for flow zone indicators prediction and hydraulic flow unit classification

Reservoir characterization, essential for understanding subsurface heterogeneity, often faces challenges due to scale-dependent variations. This study addresses this issue by utilizing hydraulic flow unit (HFU) zonation to group rocks with similar petrophysical and flow characteristics. Flow Zone Indicator (FZI), a crucial measure derived from pore throat size, permeability, and porosity, serves as a key parameter, but its determination is time-consuming and expensive. The objective is to employ supervised and unsupervised machine learning to predict FZI and classify the reservoir into distinct HFUs. Unsupervised learning using K-means clustering and supervised algorithms including Random Forest (RF), Extreme Gradient Boosting (XGB), Support Vector Machines (SVM), and Artificial Neural Networks (ANN) were employed. FZI values from RCAL data formed the basis for model training and testing, then the developed models were used to predict FZI in unsampled locations. A methodical approach involves 3 k-fold cross-validation and hyper-parameter tuning, utilizing the random search cross-validation technique over 50 iterations was applied to optimize each model. The four applied algorithms indicate high performance with coefficients determination (R2) of 0.89 and 0.91 in training and testing datasets, respectively. RF showed the heist performance with training and testing R2 values of 0.957 and 0.908, respectively. Elbow analysis guided the successful clustering of 212 data points into 10 HFUs using k-means clustering and Gaussian mixture techniques. The high-quality reservoir zone was successfully unlocked using the unsupervised technique. It has been discovered that the areas between 2370–2380 feet and 2463–2466 feet are predicted to be high-quality reservoir potential areas, with average FZI values of 500 and 800, consecutively. The application of machine learning in reservoir characterization is deemed highly valuable, offering rapid, cost-effective, and precise results, revolutionizing decision-making in field development compared to conventional methods.

Estimating the contribution of thermal processes to generation of seismicity in the junction zone of the Chuya Depression and the Kyrgyz Ridge of the Northern Tien Shan

This paper presents the attempt to estimate the contribution of thermal processes in the Earth’s crust to the generation of seismicity by the example of the seismically active region of the Chuya depression and the Kyrgyz ridge of the Northern Tien Shan. For this purpose, we use the models of temperature, petrophysical characteristics, and elastic moduli constructed in the previous works. The silica content model based on seismic tomography data is used to build a lithotype model. The constructed thermal conductivity model is utilized, along with the temperature model, to create a depth model of heat flow density. Based on the density, elastic moduli, and temperature models, the shear and thermoelastic stress models in the rocks are constructed. Their comparison with the distribution of earthquake hypocenters suggests that on the scale of the seismically active zone of the Chuya depression and the Kyrgyz ridge of the Northern Tien Shan, seismicity is mainly determined by the thermomechanical effect resulting from the hot ascending flow of acid magma from the upper mantle beneath the Muyunkum–Narat median mass.

Integrated Petrophysical Evaluation and Rock Physics Modeling of Broom Creek Deep Saline Aquifer for Geological CO2 Storage

Fossil fuels, such as coal and hydrocarbons, are major drivers of global warming and are primarily responsible for worldwide greenhouse gas emissions, including carbon dioxide CO2. The storage of CO2 in deep saline reservoirs is acknowledged as one of the top practical and promising methods to reduce CO2 emissions and meet climate goals. The North Dakota Industrial Commission (NDIC) recently approved the fourth Class VI permit for a carbon capture and storage project in the Williston basin of North Dakota for the geological CO2 storage in the Broom Creek formation. The current research aimed to conduct a comprehensive petrophysical characterization and rock physics modeling of the Broom Creek deep saline reservoir to unravel the mineralogical distribution and to understand the variations in petrophysical and elastic properties across the formation. This study utilized geophysical well logs, routine core analysis, and advanced core analysis to evaluate the Broom Creek formation. Multimineral petrophysical analysis calibrated with X-ray diffraction results reveals that this formation primarily comprises highly porous clean sandstone intervals with low-porosity interspersed with dolomite, anhydrite, and silt/clay layers. The formation exhibits varying porosities up to 0.3 and Klinkenberg air permeabilities up to ∼2600 mD. The formation water resistivity using Archie’s equation is approximately 0.055 ohm-m at 150 °F, corresponding to around 63,000 ppm NaCl salinity, which is consistent with prior data. The pore throat distribution in the samples from clean sandstone intervals is primarily situated in the macro-mega scales. However, the presence of anhydrite and dolomite impedes both porosity and pore throat sizes. The accurate prediction of effective elastic properties was achieved by developing a rock physics template. Dry rock moduli were modeled using Hill’s average, while Berryman’s self-consistent scheme was employed for modeling saturated moduli.

Petrographical and petrophysical characterization of pre-salt Aptian carbonate reservoirs from the Santos Basin, Brazil

Reservoir quality in carbonates is influenced by various factors such as the depositional environment, burial history and diagenesis processes. Understanding these geological heterogeneities is essential for successful petroleum exploration. This study characterizes Brazilian pre-salt reservoirs and aims to understand how their heterogeneity impacts reservoir quality. We analysed carbonate samples from the Barra Velha Formation (Santos Basin) through an integration of petrographical and core plug descriptions, petrographical facies characterization, porosity and permeability measurements, and image analysis to identify the principal controls on porosity and permeability, pore-size distribution, and groups with similar petrophysical properties using the hydraulic flow unit (HFU) concept. Five facies groups were recognized: Spherulitestone (F1); Shrubstone (F2); Intraclastic Grainstone (F3); Intraclastic Packstone, Spherulitestone with mud and Shrubstone with mud (F4); and Shrub–Spherulite Intercalations and Bioclastic Grainstone (F5). The analysis of porosity and permeability showed that their variations are associated with pore type and cementation rate. Greater contributions of inter-aggregate, interparticle and vugular porosity, combined with a reduced amount of cement, results in higher porosity and permeability but an increase in cement tends to reduce the porosity and permeability. Among the facies groups, F2 and F3 exhibited the best porosities and permeabilities, followed by F1, F4 and F5. From image analysis, small pores (1.5 × 10 −5 –0.01 mm 2 ) are the most common in all rocks. However, these small pores contributed significantly to total porosity only in F4 and some samples of F3. For F2 and F3, the large pores (from 0.01 mm 2 to a maximum of 19.62 mm 2 ) are the main contributors, while F5 has a homogeneous contribution. Finally, the data were grouped into five HFUs: HFU1 and HFU2 represent the zones with the best reservoir quality, primarily composed of F2 and F3.