Petrophysical Research Articles

An integrated approach of petrophysical analysis and rock physics modeling to distinguish the Upper Cretaceous sandstone heterogeneity in the Muglad rift basin of Sudan

An integrated approach of petrophysical analysis and rock physics modeling to distinguish the Upper Cretaceous sandstone heterogeneity in the Muglad rift basin of Sudan

Tractor Three-Point Hitch Control for an Independent Lower Arms System

The three-point hitch, found on agricultural tractors, facilitates the raising and lowering of an attached implement. Some tractors include a rock shaft that comprises a physical shaft that interconnects and facilitates the raising and lowering of the lower arms of the three-point hitch in a synchronized manner. In this study, we deal with a hitch system with the lower arms actuated by two independent hydraulic cylinders. This innovative tractor hitch system design allows the implement to follow the terrain, instead of the tractor, about the fore–aft (roll) axis of the tractor. However, since the two lower arms are independent, a specialized controller is needed to move these arms in unison. First, we present a position controller for individual arms and a roll controller to move these arms together. Second, we present a unique algorithm to emulate a physical rock shaft while the implement is operating in float mode. The algorithm ensures that the implement does not roll around the fore–aft axis while making sure it moves up and down vertically to follow the terrain. We present experimental results from the step response of the hitch system’s height while tracking a velocity reference. With the roll of the implement defined as the difference between the left arm’s position in percentage and that of the right arm in percentage, we observe that the largest mean roll was 0.23% with a flail mower attached and 0.26% without any implement. We then present results from the implement’s position in the float mode when the software rock shaft was activated and compare them with the case without the software rock shaft. The experiments showed that, when the software rock shaft was turned on, the mean roll reduced from 4.64% to 0.58% with a seed drill implement and from −3.99% to −0.59% with a flail mower implement. The standard deviations in these two implement cases improved from 16.77% to 2.79% and 6.45% to 3.53%, respectively, proving the effectiveness of the software rock shaft and its potential to replace the physical rock shaft found on the traditional tractors.

Application and prospective of sand-type 3D printing material in rock mechanics: a review

PurposeThis paper aims to investigate the application of 3D printing technology, particularly using sand-type materials, in the creation of artificial rock models for rock mechanics experimentation.Design/methodology/approachUsing a comprehensive analysis, this research explores the utilization of 3D printing technology in rock mechanics. Sand-type materials are specifically investigated for their ability to replicate natural rock characteristics. The methodology involves a review of recent achievements and experimentation in this field.FindingsThe study reveals that sand-type 3D printing materials demonstrate comparable properties to natural rocks, including brittle characteristics, surface roughness, microstructural features and crack propagation patterns.Research limitations/implicationsWhile the research establishes the viability of sand-type 3D printing materials, it acknowledges limitations such as the need for further exploration and validation. Generalizability may be constrained, warranting additional research to address these limitations.Originality/valueThis research contributes insights into the potential application of sand-type 3D printing materials in indoor rock physics experiments. The findings may guide future endeavors in fabricating rock specimens with consistent structures for practical rock mechanics applications.

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.

A MATLAB approach for developing digital rock models of heterogeneous limestones for reactive transport modeling

Porosity is a key parameter controlling the physico-chemical behavior of porous rocks. Digital rock physics offers a unique technique for imaging the inherently heterogeneous rock microstructure at fine spatial resolutions and its computational reconstruction, through which a better understanding and prediction of the rock behavior can be achieved. In this study, we propose a simple but accurate method to build a 3D porosity map of centimeter-scale carbonate rock cores from X-ray Micro Computed Tomography (XMCT) imaging data. The method consists of 3 main steps: i) decomposition of 3D volumetric XMCT data into sub-volumes, ii) processing of equidistributed 2D cross-section images in each sub-volume and iii) 2D slice-by-slice calculation of porosity and its assembly to reconstruct a 3D continuum porosity map over the whole core domain using a MATLAB code. The proposed approach significantly conserves the required memory to manipulate large image datasets. The digitized porosity representations are used to build 3D permeability maps of the cores by applying an explicit permeability-porosity relationship. The permeability maps are used as input for numerical simulation of the rock response to the percolation of reactive fluids through which the general validity of the approach is verified. The developed digital rock model paves the way for an improved understanding of reactive transport in carbonate rocks.

Rock physics modeling to evaluate clastic reservoirs: a case study from the Lower Goru Formation, Middle Indus Basin, Pakistan

Rock physics modeling to evaluate clastic reservoirs: a case study from the Lower Goru Formation, Middle Indus Basin, Pakistan

Building a carbon storage portfolio for the Barrow and Dampier sub-basins – a national resource

The Australian government has recently legislated a 43% emissions reduction target for 2030 to drive net zero ambitions for 2050. The government plans for carbon capture and storage (CCS) to play a significant role in achieving this through greenhouse gas storage licence rounds. Rapid screening of Australia’s carbon storage opportunities is necessary to position future acreage release and ultimately identify and mature carbon storage sites. An industry collaboration between Geoscience Australia and SLB has conducted a basin-scale carbon storage assessment project for the Barrow and Dampier sub-basins. The project has built and modernised a subsurface database fit for CCS screening. The dataset was used as the foundation to create a carbon storage opportunity portfolio and storage resource assessment for the area. The project has three major streams: (1) CCS-focussed reprocessing, depth imaging and merging of 29 legacy seismic surveys covering 26,150 km2 – focussed on detailed image resolution for containment analysis. (2) A basin-wide audit of 1620 wells and databasing of available log data, 129 wells were carried through for petrophysical and rock physics analysis relevant to CCS. (3) A portfolio of carbon storage opportunities and associated capacity assessment for the Barrow–Dampier including saline aquifers and depleted fields ranked on storage capacity, reservoir and seal properties and integrity. The results of this study will be released via the National Offshore Petroleum Information Management System as a public resource to expedite CCS implementation for the region.

Shear-wave attenuation anisotropy: a new constraint on mantle melt near the Main Ethiopian Rift

The behaviour of fluids in preferentially aligned fractures plays an important role in a range of dynamic processes within the Earth. In the near-surface, understanding systems of fluid-filled fractures is crucial for applications such as geothermal energy production, monitoring CO2 storage sites, and exploration for metalliferous sub-volcanic brines. Mantle melting is a key geodynamic process, exerting control over its composition and dynamic processes. Upper mantle melting weakens the lithosphere, facilitating rifting and other surface expressions of tectonic processes.Aligned fluid-filled fractures are an efficient mechanism for seismic velocity anisotropy, requiring very low volume fractions, but such rock physics models also predict significant shear-wave attenuation anisotropy. In comparison, the attenuation anisotropy expected for crystal preferred orietation mechanisms is negligible or would only operate outside of the seismic frequency band.Here we demonstrate a new method for measuring shear-wave attenuation anisotropy, apply it to synthetic examples, and make the first measurements of SKS attenuation anisotropy using data recorded at the station FURI, in Ethiopia. At FURI we measure attenuation anisotropy where the fast shear-wave has been more attenuated than the slow shear-wave. This can be explained by the presence of aligned fluids, most probably melts, in the upper mantle using a poroelastic squirt flow model. Modelling of this result suggests that a 1% melt fraction, hosted in aligned fractures dipping ca. 40° that strike perpendicular to the Main Ethiopian Rift, is required to explain the observed attenuation anisotropy. This agrees with previous SKS shear-wave splitting analysis which suggested a 1% melt fraction beneath FURI. The interpreted fracture strike and dip, however, disagrees with previous work in the region which interprets sub-vertical melt inclusions aligned parallel to the Main Ethiopian Rift which only produce attenuation anisotropy where the slow shear-wave is more attenuated. These results show that attenuation anisotropy could be a useful tool for detecting mantle melt, and may offer strong constraints on the extent and orientation of melt inclusions which cannot be achieved from seismic velocity anisotropy alone.

Multiscale fracture, physical and mechanical properties of stromboli volcano (Italy) edifice

The physical, mechanical and fracture properties at Stromboli volcano have been integrated at multiple scales to understand whether the interplay between a presumed NE/SW rift zone and the Sciara del Fuoco (SDF) depression has resulted in a zone of weakness able to promote fracturing prone to flank instability. Multiscale fracture quantification by imaging via FracPaQ toolbox both fractures and sample scale fractures has been integrated with rock physics and rock mechanics experiments on cm-scale samples belonging to the Paleostromboli, Vancori, Neostromboli, Pizzo and Present Deposit volcanic cycles that have been taken from within and outside the rift zone. The structural changes to the edifice have been quantitively assessed by mapping at different scale fracture properties such density and orientation within and outside the rift zone allowing to identify the potential damaged zones that could reduce the edifice strength.Results indicate that basalt textures, microfracture density, porosity, chemical zoning and preferential alignments, despite lithologically dependent, can be related to the NE/SW zone of weakness at the regional scale and to collapsed volumes that have been subject to continuous intrusive activity. Numerical inversion models have been performed to cross correlate fracture density in the basalts at multiple scales.A link between microfracture density and seismic velocities has been also established via numerical modelling, allowing to interpret in terms of degree of fracturing the results of seismic tomographies at the field scale, providing a novel method to image crack damage evolution within the inner structure of the volcano edifice.

Permeability modelling in a highly heterogeneous tight carbonate reservoir using comparative evaluating learning-based and fitting-based approaches

Permeability modelling is considered a complex task in reservoir characterization and a key component of reservoir simulation. A common method for permeability modelling involves performing static rock typing (SRT) using routine core analysis data and developing simple fitting-based mathematical relations that link permeability to reservoir rock porosity. In the case of carbonate reservoirs, which are associated with high heterogeneities, fitting-based approaches may fail due to porosity–permeability data scattering. Accurate modelling of permeability using petrophysical well log data seems more promising since they comprise a vast array of information about the intrinsic properties of the geological formations. Furthermore, well log data exhibit continuity throughout the entire reservoir interval, whereas core data are discrete and limited in availability and coverage. In this research work, porosity, permeability and log data of two oil wells from a tight carbonate reservoir were used to predict permeability at un-cored intervals. Machine learning (ML) and fitting models were used to develop predictive models. Then, the developed ML models were compared to exponential and statistical fitting modelling approaches. The integrated ML permeability model based on Random Forest method performed significantly superior to exponential and statistical fitting-based methods. Accordingly, for horizontal and vertical permeability of test samples, the Root Mean Squared Error (RMSE) values were 3.7 and 4.5 for well 2, and 1.7 and 0.86 for well 4, respectively. Hence, using log data, permeability modelling was improved as it incorporates more comprehensive reservoir rock physics. The outcomes of this reach work can be used to improve the distribution of both horizontal and vertical permeability in the 3D model for future dynamic reservoir simulations in such a complex and heterogeneous reservoir system.

Introduction to this special section: Rock physics

As a means of interpreting subsurface geophysical and petrophysical data in terms of engineering reservoir properties, the discipline of rock physics has been central to delivering value for an ever-growing pool of underground energy applications. As the industry looks in new directions, from the sequestration of carbon dioxide to the production and storage of geothermal heat and hydrogen, many new challenges arise that position rock physics as a major enabler to a new sustainable energy portfolio. Delivering on that promise will in part necessitate the ingenuity and dedication of a new generation of rock physicists as skilled in the understanding of fundamental chemo-physical processes at play as they are in the astute use of modern digital tools. But as with any great project, foundation comes first, and long-recognized challenges that have already prompted decades of rock physics modeling efforts still require our attention if we are to succeed in these new endeavors.

A new rock physics model for fractured oil shale reservoirs in Mahu oil field

The exploration of unconventional reservoirs such as oil shale has become a focus of research in the oil/gas industry, but due to the diversity of lithology and structural complexity of shale reservoirs, the study of their rock physics laws is a challenge. By analyzing the physical, lithologic, and fluid characteristics of oil shale reservoirs in the Mahu area of Xinjiang, China, we adopted a variety of effective-medium theories to carry out rock physics modeling. We analyzed the differences in the calculation results of various theoretical models, and finally constructed a set of rock physics modeling processes suitable for oil shale reservoirs. The analysis shows that the calculation results of Voigt-Reuss-Hill (VRH) and Hashin-Shtrikman (HS) average for mixed mineral matrix are very similar, but there are certain differences between the upper and lower bounds calculated by them. The upper and lower bounds of HS average are closer than VRH average. We used the Kuster-Toksoz effective-medium, differential effective-medium (DEM), and self-consistent approximation models to construct the rock skeleton and analyzed the differences of the models. The results of the three models are almost identical, but their assumptions and limitations differ. We used DEM in the final shale model, considering the large porosity and the order of inclusion filling. According to the fracture distribution characteristics of oil shale reservoirs, we used an inclined fracture model to describe the fractures with different structural characteristics. Finally, the established shale rock physics model was used to calculate the actual logging data, and the results of elastic parameters are consistent with the measured data. Through the inversion of the fracture parameters, the inversion results are consistent with the measured results as a whole, indicating that the model has certain applicability to the simulation of oil shale reservoirs.

Velocity Measurements of Powdered Rock at Low Confining Pressures and Comparison to Lunar Shallow Seismic Velocity

AbstractSeismic methods will be useful for future lunar near‐surface characterization, and high‐fidelity elastic models will be required to aid interpretation of seismic observations. To develop an elastic lunar near‐surface model, we performed ultrasonic velocity measurements of lunar regolith simulant at low confining pressure and developed a rock physics model calibrated to these measurements. Grain contact models based on Hertz‐Mindlin theory produce accurate results at high confining pressure (i.e., several hundred meters or more burial depth) but historically fail to predict observed velocities in unconsolidated media at low pressure. Therefore, we heuristically modified existing models to fit our measured data over a range of porosities and confining pressures. To compare with Apollo 14 and 16 active seismic experiments, we used our new heuristic rock physics model to produce lunar subsurface velocity profiles. We performed ray tracing through our velocity profiles to calculate seismic traveltime, which results in good agreement with first arrivals interpreted from the Apollo experiments. Our model suggests a slightly higher velocity‐pressure dependence than inferred from in situ measurements, which may be due to porosity reduction in the lunar regolith from impact‐induced and natural vibrations.

Rock physics-based anisotropy parameters estimation of Marcellus Shale using log data acquired in a vertical well

The elastic properties of shale rock exhibit anisotropy due to the presence of highly anisotropic clay minerals and their alignment. This anisotropy has a significant impact on geophysical and geomechanical responses. However, the elastic anisotropy of shale is often overlooked due to the difficulty in measuring sufficient parameters in the field, resulting in a gap between theoretical understanding and practical implementation. To address this issue, a rock physics-based approach was tested on log data obtained from a vertical well in the Marcellus Shale to estimate anisotropy parameters. The approach utilized a rock physics model based on the Sayers-den Boer method with model parameters optimized using measured stiffness parameters C33, C55, and C66. Anisotropy parameters δ and ε were then calculated using these optimized model parameters. The optimized model parameters and estimated anisotropy parameters are consistent with existing studies, indicating the correctness of the model. Additionally, the rock physics model was used to investigate the impact of gas saturation on the vertical VP/VS ratio and ε/γ ratio. It was found that gas saturation has a significant impact on these ratios. This carries important implications for acoustic log data interpretation and seismic characterization of shales.

Impact of Fluid Distribution and Petrophysics on Geophysical Signatures of CO2 Storage Sandstone Reservoirs

AbstractCarbon Capture and Storage (CCS) is a key element to achieving net‐zero energy challenge timely. CCS operations require the integration of geophysical data, such as seismic and electromagnetic surveys, numerical reservoir models and fluid flow simulations. However, the 10–100s m resolution of seismic imaging methods complicates the mapping of smaller scale rock heterogeneities, while borehole measurements commonly show large fluctuations at sub‐cm scales. In this study, we combine laboratory data, well‐logging, rock physics theories and a proof‐of‐concept time‐lapse seismic modeling to assess the effect of pore‐scale fluid distribution and petrophysical heterogeneities on the expected performance of whole‐reservoir CCS operations in deep saline aquifers, by analogy to the Aurora CCS site, North Sea. We monitored the elastic and electrical properties of three sandstone samples with slightly different physical and petrographic properties during carbon dioxide (CO2) flow‐through tests under equivalent in situ effective pressure. We inferred the CO2‐induced damage in the rocks from the variations of their hydromechanical properties. We found that the clay fraction, CO2‐clay chemical interactions, and porosity were the main factors affecting both the CO2 distribution in the samples and the hydromechanical response. We used seismic modeling of well‐log data and the laboratory results to estimate the reservoir‐scale time‐lapse seismic response to CO2 injection and to assess the effect of the rock heterogeneities in our interpretation. The results show that disregarding the effect of rock heterogeneities on the CO2‐brine fluids distribution can lead to significant misinterpretations of seismic monitoring surveys during CCS operations in terms of both CO2 quantification and distribution.

An anisotropic rock physics modeling for the coalbed methane reservoirs and its applications in anisotropy parameter prediction

The elastic properties and anisotropy of coalbed methane reservoirs are still poorly understood due to the complicated pore structure and the existing state of the coalbed methane. Therefore, an anisotropic rock physics model for the coalbed methane reservoirs is proposed based on the related effective medium theories, which focuses on the equivalent calculation of the elastic properties for adsorbed coalbed methane and the characterization of the multiple pore structure. Many factors related to the elastic properties of coal are considered, including the composition, pore structure, adsorbed gas content and pore fluid. The measured ultrasonic and well-logging data demonstrate that the proposed physics model is effective in predicting the elastic constants and velocity anisotropy parameters. The results suggest that the elastic constants increase with the increase of adsorbed gas content to some extent, and the effect of porosity on the elastic constants is much more obvious than that of adsorbed gas content. The velocity anisotropy parameters increase with the aligned fracture porosity and the fracture aspect ratio, and the P-wave anisotropy is more sensitive to these two factors. With increasing organic matter and clay content and porosity, Young's modulus and brittleness index decrease while the Poisson's ratio increases, and the impact of the porosity on the brittleness index is more significant than that of organic matter and clay content. The results are helpful in establishing the relationship between reservoir physics parameters and seismic response and can provide a basis for coalbed methane reservoir prediction and hydraulic fracturing.

Basin‐scale prediction of S‐wave Sonic Logs using Machine Learning techniques from conventional logs

AbstractS‐wave velocity plays a crucial role in various applications but often remains unavailable in vintage wells. To address this practical challenge, we propose a machine learning framework utilizing an enhanced bidirectional long short‐term memory algorithm for estimating S‐wave sonic logs from conventional logs, including P‐wave sonic, gamma ray, total porosity, and bulk density. These input logs are selected based on traditional rock physics models, integrating geological and geophysical relations existing in the data. Our study, encompassing 34 wells across diverse formations in the Delaware Basin, Texas, demonstrates the superiority of machine learning models over traditional methods like Greenberg–Castagna equations, without prior geological and geophysical information. Among these machine learning models, the enhanced bidirectional long short‐term memory model with self‐attention yields the highest performance, achieving an R‐squared value of 0.81. Blind tests on five wells without prior geologic information validate the reliability of our approach. The estimated S‐wave velocity values enable the creation of a basin‐scale S‐wave velocity model through interpolation and extrapolation of these prediction models. Additionally, the bidirectional long short‐term memory model excels not only in predicting S‐wave velocity but also in estimating S‐wave reflectivity for seismic amplitude variation with offset applications in exploration seismology. In conclusion, these S‐wave velocity estimates facilitate the prediction of further elastic properties, aiding in the comprehension of petrophysical and geomechanical property variations within the basin and enhancing earthquake hypocentral depth estimation.

Effective Elastic Properties and Micro-mechanical Damage Evolution of Composite Granular Rocks: Insights from Particulate Discrete Element Modelling

The mechanical behavior of composite granular rocks is a multifaceted phenomenon with broad relevance in various geomechanical applications. Traditional homogenization models and continuum mechanics-based numerical methods often fall short of accurately capturing the intricacies of granular materials. Granular materials exhibit heterogeneity and arching mechanisms governing the force networks that ensure system stability. Unlike continuum-based approaches, discrete element methods (DEM) have an advantage in assessing effective material properties by considering material heterogeneity and grain-level physical interactions. This study evaluates effective elastic properties using DEM with flat-joint contact law for composite binary mixtures with a stiff inclusion embedded within a matrix material. We examine variations in the elastic properties across different structural and laminated geometrical distributions of inclusion materials. Our findings closely adhere to the Voigt-Reuss and Hashin–Strikman bounds within their specific conditions, demonstrating the promising application of DEM in the analysis of composite materials. In addition, our research provides an in-depth analysis of the distinctive stress-evolution and damage-evolution characteristics exhibited by various geometrical configurations of inclusions under unconfined compressive loading. These results offer invaluable insights into the mechanical behavior of composite granular rocks and underscore the potential applications of DEM in addressing rock physics modeling problems encountered in petroleum engineering.

Simultaneous prediction of petrophysical properties and formation layered thickness from acoustic logging data using a modular cascading residual neural network (MCARNN) with physical constraints

Predicting petrophysical properties from logging data using deep neural networks (DNNs) improves the efficiency of subsurface exploration. Traditional methods, however, heavily rely on data pre-processing. Therefore, a novel Modular Cascading Residual Neural Network (MCARNN) is proposed to simultaneously retrieve layered thickness and petrophysical properties of porous media directly from raw acoustic logging data without pre-processing. The MCARNN mainly comprises two cascading modules: the first is tasked with retrieving layered thickness, while the second one further reconstructs petrophysical properties based on the thickness information. To more accurately simulate the uncertainties of the real world, we incorporate some known geophysical knowledge, including the distribution range of actual petrophysical parameters, physical constraints among porous medium parameters, configurations of logging instruments, and stratified geological models. During this process, all Biot's porous parameters are independently randomized to cover a wide range of geological rock types. Furthermore, to enhance MCARNN's generalization ability under different random noise distributions, we independently generate the training and testing datasets using distinct numerical algorithms, thus preventing the “inverse crime” phenomenon. Additionally, varying levels of Gaussian white noises are added to the training dataset, and the transmitter-receiver configuration of the logging instrument is varied to enhance the training dataset. Two numerical experiments are implemented to validate the reliability and superiority of the MCARNN. It is shown that the proposed MCARNN can not only accurately predict porous petrophysical parameters when the background layer configurations are known but also be able to simultaneously reconstruct layered thickness and petrophysical parameters with low model misfit. Notably, MCARNN successfully predicts the porous velocity dispersion curves within a broad frequency band based on limited frequency points—a task typically requiring auxiliary equations in traditional inversion methods, demonstrating the advanced predictive capability of the proposed MCARNN. Through this research, we not only address the deficiencies in existing real-world logging datasets in terms of sample volume and label reliability but also effectively integrate rock physics models with data-driven neural networks, providing a novel and effective pathway for reservoir parameter prediction in the field of geophysical logging.

Introduction to special section on the rock physics contribution to the energy transition challenge

Introduction to special section on the rock physics contribution to the energy transition challenge