Petrophysical Relationships Research Articles

Coupled Hydrogeophysical Modeling to Constrain Unsaturated Soil Parameters for a Slow‐Moving Landslide

AbstractGeophysical methods have proven to be useful for investigating unstable slopes as they are both non‐invasive and sensitive to the spatial distribution of physical properties in the subsurface. Of particular interest are the links between electrical resistivity and near‐surface moisture content; recent work has demonstrated that it is possible to calibrate hydrological models using geophysical measurements. In this study we explore the use of in‐field electrical resistivity data for calibrating unsaturated soil retention parameters and saturated hydraulic conductivity used for modeling unsaturated fluid flow. We study a synthetic case study, and a well‐characterized site in the northeast of England and develop an approach to calibrate retention parameters for a mudstone and a sandstone formation, the former being an actively failing unit. Petrophysical relationships between electrical resistivity and moisture content (or saturation) are established for both formations. 2D hydrological models are driven by effective rainfall estimations; subsequently these models are coupled with a geophysical forward model via a Markov chain Monte Carlo approach. For the synthetic case, we show that our modeling approach is sensitive to the moisture retention parameters, while less so to saturated hydraulic conductivity. We observe the same characteristics and sensitivities for the field case, albeit with a greater data misfit. Further hydrological simulations suggest that the slope retained high moisture contents in the months preceding a rotational failure. Therefore, we propose that coupled hydrological and geophysical modeling approaches could aid in enhancing landslide monitoring, modeling, and early warning efforts.

The 3D Crustal Structure of the Wilkes Subglacial Basin, East Antarctica, Using Variation of Information Joint Inversion of Gravity and Magnetic Data

AbstractDirect geological information in Antarctica is limited to ice free regions along the coast, high mountain ranges, or isolated nunataks. Therefore, indirect methods are required to reveal subglacial geology and heterogeneities in crustal properties, which are critical steps toward interpreting geological history. We present a 3D crustal model of density and susceptibility distribution in the Wilkes Subglacial Basin (WSB) and the Transantarctic Mountains (TAM) based on joint inversion of airborne gravity and magnetic data. The applied “variation of information” technique enforces a coupling between inferred susceptibility and density, relating these quantities to the same gravity and magnetic sources to give an enhanced inversion result. Our model reveals a large body located in the interior of the WSB interpreted as a batholithic intrusive structure, as well as a linear dense body at the margin of the Terre Adélie Craton. Density and susceptibility relationships are used to inform the interpretation of petrophysical properties and the reconstruction of the origin of those crustal bodies. The petrophysical relationship indicates that the postulated batholitic intrusion is granitic, but independent from the Granite Harbor Igneous Complex described previously in the TAM area. Emplacement of a large volume of intrusive granites can potentially elevate local geothermal heat flow significantly. Finally, we present a new conceptual tectonic model based on the inversion results, which includes development of a passive continental margin with seaward dipping basalt horizons and magmatic underplating followed by two distinct intrusive events associated with the protracted Ross Orogen.

Soil water content estimation by using ground penetrating radar data full waveform inversion with grey wolf optimizer algorithm

AbstractSoil water content (SWC) estimation is important for many areas including hydrology, agriculture, soil science, and environmental science. Ground penetrating radar (GPR) is a promising geophysical method for SWC estimation. However, at present, most of the studies are based on partial information of GPR, like travel time or amplitude information, to invert the SWC. Full waveform inversion (FWI) can use the information of the entire waveform, which can improve the accuracy of parameter estimation. This study proposes a novel SWC estimation scheme by using the FWI of GPR, optimized by the grey wolf optimizer (GWO) algorithm. The proposed scheme includes a petrophysical relationship to link the SWC with the relative dielectric permittivity, 1D GPR forward modeling, and a GWO optimization algorithm. First, numerical modeling was carried out, and the proposed scheme was applied to both noise‐free and noisy data to verify its applicability. Then, the proposed method was applied to data collected from a field experimental site. These results, derived from both synthetic and real datasets, show that the proposed inversion scheme resulted in a good match between the observed and calculated GPR data. In the numerical modeling, it was observed that the SWC could be inverted accurately, even when noise was present in the data. These demonstrate that the GWO method can be applied for the quantitative interpretation of GPR data. The proposed scheme shows potential for SWC estimation by using GPR full waveform data.

Physics‐Informed Neural Networks Trained With Time‐Lapse Geo‐Electrical Tomograms to Estimate Water Saturation, Permeability and Petrophysical Relations at Heterogeneous Soils

AbstractDetermining soil hydraulic properties is complex, posing ongoing challenges in managing subsurface and agricultural practices. Electrical resistivity tomography (ERT) is an appealing geophysical method to monitor the subsurface due to its non‐invasive, easy‐to‐apply and cost‐effective nature. However, obtaining geoelectrical tomograms from raw measurements requires the inversion of an ill‐posed problem, which causes smoothing of the actual structure. Furthermore, the spatial resolution is determined from the distances in the electrode placement, thus inherently upscaling the obtained structure. This study explores the applicability of physics‐informed neural networks (PINNs) for upscaling permeability and petrophysical relations and monitoring water dynamics at heterogeneous soils using time‐lapse geoelectrical data. High‐resolution numerical simulations mimicking water infiltration were used as benchmarks. Synthetic ERT surveys with electrode spacing 10 times larger than the numerical model resolution were conducted to provide 2D electrical tomograms. The tomograms were fed to a PINNs system to obtain the permeability, petrophysical relations, and water content maps. An additional PINNs system incorporating water content measurements was trained to examine measurement sensitivity. Results have shown that the PINNs system could produce reliable results regarding the upscaled permeability and petrophysical relations fields. Water dynamics at the subsurface was accurately predicted with an average error of ∼3%. Adding water content measurements to PINNs training improved the system outcomes, mainly at the ERT low sensitivity zones. The PINNs system reduced water saturation errors by more than 30% compared to the common practice of directly translating the geoelectrical tomograms to water saturations using known, homogeneous petrophysical relations.

Improving deep groundwater aquifer characterization with deep learning inversion of audio-frequency magnetotelluric data

Deep groundwater is a crucial resource for drinking, industry, and ecosystems. However, its extensive subsurface distribution poses challenges for traditional hydrological sampling methods. Audio-frequency Magnetotelluric (AMT) is commonly used to image shallow subsurface resistivity distribution, but its application for quantifying deep groundwater aquifers is challenging. In this study, we propose a two-step strategy deep learning (DL) to estimate subsurface water information from AMT data. First, we employ an inversion DL network to directly predict subsurface resistivity distribution from AMT data. To assess the impact of data and prior information on DL inversions, we progressively include more input data during training, from apparent resistivity to apparent resistivity, phase, and Bostick transformed initial models. In the second step, we use another DL network to predict subsurface structure from AMT inversion results. We then estimate water content based on unit-specific petrophysical relationships. Evaluating our algorithm with synthetic cases, we find that including more information generally improves network performance, particularly when incorporating initial Bostick transformed models. Applying the algorithm to a field hydrogeological survey, we compare the inversion network trained with apparent resistivity, phase, and Bostick transformed initial models to traditional regularization inversions. The new approach shows better consistency with borehole data. Another network extracts structure information, enabling the estimation of subsurface water content and gaining valuable hydrogeological insights. To summarize, our study presents a novel DL-based approach to quantitatively delineate deep groundwater aquifers using AMT data. The proposed algorithm demonstrates promising performance in estimating subsurface hydrologic properties, providing valuable insights for hydrogeological investigations.

Charakterystyka złoża formacji Baharyia, pola naftowe Neag-1, 2 i 3, Pustynia Zachodnia, Egipt

An integration was achieved between different bore holes and laboratory measured data using several petrophysical parameters of the Baharyia Formation encountered in Neag-1,2&3 oil fields. It illustrates the key control factors affecting the Baharyia reservoir quality. The obtained petrophysical relationships could be used widely in both exploration geophysics and hydrocarbon reservoir production. It provides and demonstrates solutions for both geological and geophysical engineering problems. The measured porosity and permeability are ranging from 2.5 to 32 % and 0.005 to 874 mD respectively. The influence of diagenesis on both reservoir porosity and permeability has been investigated. Pore filling minerals has been classified into four classes by XRD- analysis technique. A reliable regression equation was reached between reservoir permeability and mineral pore fillings. Several relationships among rock permeability, porosity and density obtained from open hole logs were recognized. The pore throat distribution has been laboratory measured by use of MICP technique for some selected samples. The calculated reservoir storage and flow capacity indicate four major fluid flow types which are controlled by the variations in reservoir pore space framework. Formation resistivity factor – porosity relation was accomplished under reservoir conditions, while the Archie’s 2nd equation was outlined. The Archie’s parameters (a, m &n) were calculated for shaly and clean sandstones of the Baharyia Formation. Both cation exchange capacity (CEC), Mounce potential (MP) and mercury injection capillary pressure (MICP) were measured to distinguish reservoir facies.

“Core–core” petrophysical relationships generation for reservoir modeling

Relevance. The need in a detailed analysis of distribution of physical properties of the formation in space. Currently, the parameters connecting the results of responses of geophysical fields and petrophysical studies of core are averaged. On the one hand, this is due to the small number of wells with cores in the fields, and on the other hand, to simplify and speed up calculations in the presence of a large number of wells with geophysical surveys of wells. However, this approach does not allow us to identify the largest number of a particular layer or section characteristics. This, in its turn, may lead to inaccuracies in calculating filtration and capacitance properties. When averaging parameters, the features of formation of the deposit in parts of the field with core sampling are lost. And this is a very big opportunity to more accurately form facies models of deposits. Aims. To generate a map of skeletal density distribution based on core data for the supra-coal strata of a terrigenous oil reservoir; analyze the resulting distribution map, identify areas with increased and decreased density values; assess the degree of change in the porosity coefficient when compared with density values; identify areas of high and low density and trends. Object. Supra-coal strata of terrigenous sediments of one of the layers of an oil field in the Tomsk region. Methods. Analysis of the petrophysical database leads to formation of an idea of the reservoir. Laboratory core studies are the source of the most reliable information about the filtration and reservoir properties of the formation. The analysis technique involves the well-by-well construction of dependencies of petrophysical parameters and determination of the value of the constant density of the skeleton. Additionally, a general relationship is built for all wells to compare values and identify maximum and minimum parameters boundaries, skeletal density distribution map and resulting zones with low- and high-density values analysis. Borehole differentiation of values leads to increased detail of the distribution of the studied parameter and identification of zones with abnormally high and low values for more detailed study and the formation of a conceptual geological model.

Practical considerations for using petrophysics and geoelectrical methods on clay rich landslides

Understanding the geological and hydrological conditions present within an unstable slope is crucial for assessing the likelihood of failure. Recently, geoelectrical characterization and monitoring of landslides has become increasingly prevalent in this context, due to the spatial sensitivity of electrical methods to critical hydro-mechanical parameters. We explore a situational relationship between resistivity and matric potential (or negative pore pressure), which is a key parameter in estimating the resistance to shear in geological materials, and gravimetric moisture content (GMC). We have chosen a well-characterized active landslide instrumented with geoelectrical monitoring technology, the Hollin Hill Landslide Observatory, situated in Lias rocks in the southern Howardian Hills, United Kingdom. We report on petrophysical relationships between porosity, GMC, electrical resistivity, and matric potential. We trial the application of these petrophysical relationships to inverted resistivity images. Ground model development is achieved through a mixture of clustering resistivity distributions and analysis of surface movements. Our findings show the shrink swell properties of clay result in a variable porosity, which is problematic for applying classic petrophysical relationships documented in the literature. Moreover, directly translating resistivity distributions into matric potential has additional challenges. Nonetheless, volumetric imaging of resistivity suggest that low shear strengths are concentrated downslope of a rotational backscarp. We infer that an accumulation of moisture drives the development of a slip surface at depth, which subsequently manifests in failure at the ground surface. We conclude that the time-lapse resistivity images alone could not be used to infer the pore pressure conditions present within the slope without development of the petrophysical relationships shown here. Therefore, we suggest that the results have practical implications for landslide monitoring with geophysical methods.

A framework for risk assessment of groundwater contamination integrating hydrochemical, hydrogeological, and electrical resistivity tomography method.

Groundwater contamination have been widely concerned. To reliably conduct risk assessment, it is essential to accurately delineate the contaminant distribution and hydrogeological condition. Electrical resistivity tomography (ERT) has become a powerful tool because of its high sensitivity to hydrochemical parameters, as well as its advantages of non-invasiveness, spatial continuity, and cost-effectiveness. However, it is still difficult to integrate hydrochemical, hydrogeological, and ERT datasets for risk assessment. In this study, we develop a general framework for risk assessment by sequentially jointing hydrochemical, hydrogeological, and ERT surveys, while establishing petrophysical relationships among these data. This framework can be used in groundwater-contaminated site and help to delineate the distribution of contaminants. In this study, it was applied to a nitrogen-contaminated site where field ERT survey and borehole information provided valuable measurement data for validating the consistency of contamination and hydrogeological condition. Risk assessment was conducted based on the refined results by the establishment of relationship between conductivity and contaminants concentration with . The contamination source was identified and the transport direction was predicted with the good agreement of = 0.965 between simulated and observed groundwater head, which can help to propose measures for anti-seepage and monitoring. This study thus enhances the reliability of risk assessment and prediction through a thought-provoking innovation in the realm of groundwater environmental assessment.

Quantifying salinity in heterogeneous coastal aquifers through ERT and IP: Insights from laboratory and field investigations

The lithological and stratigraphical heterogeneity of coastal aquifers has a great influence on saltwater intrusion (SI). This makes it difficult to predict SI pathways and their persistence in time. In this context, electrical resistivity tomography (ERT) and induced polarization (IP) methods are receiving increasing attention regarding the discrimination between saltwater-bearing and clayey sediments. To simplify the interpretation of ERT data, it is commonly assumed that the bulk conductivity mostly depends on the conductivity of pore-filling fluids, while surface conductivity is generally disregarded in the spatial and temporal variability of the aquifers, particularly, once the aquifer is affected by the presence of saltwater. Quantifying salinities based on a simplified petrophysical relationship can lead to misinterpretation in aquifers constituted by clay-rich sediments. In this study, we rely on co-located data from drilled boreholes to formulate petrophysical relationships between bulk and fluid conductivity for clay-bearing and clay-free sediments. First, the sedimentary samples from the drilled wells were classified according to their particle size distribution and analyzed in the lab using spectral IP in controlled salinity conditions to derive their formation factors, surface conductivity, and normalized chargeability. Second, the deduced thresholds are applied on the field to distinguish clay-bearing sediments from brackish sandy sediments. The results are validated with logging data and direct salinity measurements on water samples. We applied the approach along the Luy River catchment and found that the formation factors and surface conductivity of the different unconsolidated sedimentary classifications vary from 4.0 to 8.9 for coarse-grained sand and clay-bearing mixtures, while normalized chargeability above 1.0 mS.m−1 indicates the presence of clay. The clay-bearing sediments are mostly distributed in discontinuous small lenses. The assumption of homogenous geological media is therefore leading to overestimating SI in the heterogeneous clay-bearing aquifers.

ERT data assimilation to characterize aquifer hydraulic conductivity heterogeneity through a heat‐tracing experiment

AbstractGeothermal energy systems, such as heat pumps relying on aquifers, use renewable sources of energy that are accessible in urban areas. It is necessary to characterize the subsurface hydraulic properties prior to the installation of such systems. In this context, a heat‐tracing experiment is a typical field test that can help with the characterization of the subsurface. During a heat‐tracing experiment, monitoring with downhole temperature sensors, water‐level pressure transducers and electrical resistivity tomography (ERT) can be used to help characterize the hydrogeological properties. Previous monitoring tools have shortcomings, such as low‐resolution data and over‐smoothing; thus, they fail to reproduce the heterogeneity of hydrogeological properties. Ensemble Kalman filter (EnKF) is a promising tool that can overcome the over‐smoothing problem to replicate the hydrogeological property heterogeneity. In this work, we proposed a new procedure to assimilate time‐lapse cross‐borehole ERT data into a numerical model of groundwater flow and heat transfer, where the groundwater is extracted and heated water is reinjected into an unconfined sandy‐gravel aquifer. The finite element model (FEFLOW 7.3) of groundwater flow and heat transfer is integrated with petrophysical relationship and electrical forward modelling (ResIPy) to estimate cross‐borehole ERT measurements. Then, the estimated apparent resistivity is assimilated to update the hydraulic conductivity model using EnKF. The results of the application of the proposed approach to an experimental site located in Quebec City (Canada) demonstrate that the heterogeneity of K is correctly reproduced as the updated K model is reasonably consistent with the lithological log. In addition, the proposed approach was able to replicate the cross‐borehole ERT field and temperature measurements. The comparison between prior and posterior distribution of K with slug test results shows that the EnKF made the final (assimilated) distribution of K move towards K values inferred with slug tests.

A multiscale accuracy assessment of moisture content predictions using time-lapse electrical resistivity tomography in mine tailings

Accurate and large-scale assessment of volumetric water content (VWC) plays a critical role in mining waste monitoring to mitigate potential geotechnical and environmental risks. In recent years, time-lapse electrical resistivity tomography (TL-ERT) has emerged as a promising monitoring approach that can be used in combination with traditional invasive and point-measurements techniques to estimate VWC in mine tailings. Moreover, the bulk electrical conductivity (EC) imaged using TL-ERT can be converted into VWC in the field using petrophysical relationships calibrated in the laboratory. This study is the first to assess the scale effect on the accuracy of ERT-predicted VWC in tailings. Simultaneous and co-located monitoring of bulk EC and VWC are carried out in tailings at five different scales, in the laboratory and in the field. The hydrogeophysical datasets are used to calibrate a petrophysical model used to predict VWC from TL-ERT data. Overall, the accuracy of ERT-predicted VWC is pm 0.03~textrm{m}^3/textrm{m}^3, and the petrophysical models determined at sample-scale in the laboratory remain valid at larger scales. Notably, the impact of temperature and pore water EC evolution plays a major role in VWC predictions at the field scale (tenfold reduction of accuracy) and, therefore, must be properly taken into account during the TL-ERT data processing using complementary hydrogeological sensors. Based on these results, we suggest that future studies using TL-ERT to predict VWC in mine tailings could use sample-scale laboratory apparatus similar to the electrical resistivity Tempe cell presented here to calibrate petrophysical models and carefully upscale them to field applications.

Density and magnetization models for the Acoculco geothermal field by joint 3D inversion

We determined density and magnetization magnitude in the Acoculco geothermal field, Mexico. For that purpose, we used terrestrial gravity and aerial magnetometry data. For the joint inversion of both sets of data, we assumed a petrophysical relation between density and magnetization that is also unknown. Along the inversion process, we estimated density, magnetization in every single prism in a 3D mesh and the coefficients from the petrophysical equation. The deeper part of the 3D model seems to describe a semicircular shape, that are apparently filled of intrusions. The difference between density and magnetization inside this semicircular shape can give us some insight about the characteristics of such intrusions, The shallowest part of the model keeps very good relation with the surface faults mapped.

Solving Geophysical Inversion Problems with Intractable Likelihoods: Linearized Gaussian Approximations Versus the Correlated Pseudo-marginal Method

A geophysical Bayesian inversion problem may target the posterior distribution of geological or hydrogeological parameters given geophysical data. To account for the scatter in the petrophysical relationship linking the target parameters to the geophysical properties, this study treats the intermediate geophysical properties as latent (unobservable) variables. To perform inversion in such a latent variable model, the intractable likelihood function of the (hydro)geological parameters given the geophysical data needs to be estimated. This can be achieved by approximation with a Gaussian probability density function based on local linearization of the geophysical forward operator, thereby, accounting for the noise in the petrophysical relationship by a corresponding addition to the data covariance matrix. The new approximate method is compared against the general correlated pseudo-marginal method, which estimates the likelihood by Monte Carlo averaging over samples of the latent variable. First, the performances of the two methods are tested on a synthetic test example, in which a multivariate Gaussian porosity field is inferred using crosshole ground-penetrating radar first-arrival travel times. For this example with rather small petrophysical uncertainty, the two methods provide near-identical estimates, while an inversion that ignores petrophysical uncertainty leads to biased estimates. The results of a sensitivity analysis are then used to suggest that the linearized Gaussian approach, while attractive due to its relative computational speed, suffers from a decreasing accuracy with increasing scatter in the petrophysical relationship. The computationally more expensive correlated pseudo-marginal method performs very well even for settings with high petrophysical uncertainty.

Monitoring and characterization of water infiltration in soil unsaturated zone through an integrated geophysical approach

This paper presents an integrated geophysical method to characterize the vadose zone by conducting lab-scale infiltration experiments, during which time-lapse electrical resistivity tomography (ERT) sections and ground penetrating radar (GPR) profiles were simultaneously and continuously captured together with soil moisture and temperature probing devices installed at various depths. Time series water content data obtained from the moisture sensors, coupled with discreetly calibrated petrophysical relationships and time-lapse datasets from ERT and GPR, were combined to monitor the spatial–temporal evolution of soil water content during wetting and drying and to track the downward progression of wetting front. Reduction in electrical resistivity as well as increment in dielectric constant were converted to rising soil moisture, and then further scaled into cumulative infiltration through integration with corresponding wetting depth as observed through the experiment. Robust estimates regarding controlling vadose zone parameters such as infiltration rate and hydraulic conductivity were subsequently achieved by fitting the results from complementary investigations with an unsaturated infiltration model. With calibrated saturated hydraulic conductivity fitted well with reported and experimentally derived ranges, it’s suggested that the model is capable of reflecting average changes in infiltration rate at the lab scale. However, the model intrinsically lacks the ability to provide spatial variations of hydraulic parameters, thereby further investigation is essential to establish a suitable model for characterizing the more complex vadose zone at field scale.

Using geophysics to guide the selection of suitable sites for establishing sustainable earthen fishponds in the Niger-Delta region of Nigeria

Water retention in earthen fishponds throughout a fish farming cycle is challenging due to climate-induced water loss via evapotranspiration, seepages, and lowering of the groundwater table. These processes depend on the soil hydrostratigraphic condition and constitute a major challenge for fish farmers in the Niger-Delta region of Nigeria, where seasonal variations cause groundwater levels to fluctuate. This study assesses the use of non-invasive geophysical methods, including electrical resistivity and induced polarization, to guide the selection of sites with appropriate hydrostratigraphic conditions for establishing earthen fishponds. We combined measurements of electrical resistivity and chargeability distributions to assess the subsurface of two earthen fishpond sites at Ugono-Abraka and Agbarha-Otor areas in the Niger-Delta region of Nigeria. Electrical soundings were acquired at ten locations, while two-dimensional electrical resistivity and Induced polarization were acquired across five transects using Schlumberger and dipole-dipole electrode configurations. The field data were inverted using IP2win, and Diprowin software. The geophysical models were combined with lithological data from soil cores to characterize the subsurface stratigraphy, while measured clay contents were used to estimate infiltration coefficients relying on established petrophysical relationships. The delineated subsurface properties at Ugono-Abraka and Agbarha-Otor show higher variations than assumed by practitioners. The complementary results of low resistivity (20–140 Ωm) and high chargeability (10–50 msec) revealed areas with clay-rich sediments. Soil samples confirmed higher clay contents of up to 10% at Ugono-Abraka and low values of 2% at Agbarha-Otor. Estimated infiltration coefficients are lower at the Ugono-Abraka site (1.6 m/day) compared to Agbarha-Otor (8.4 m/day). This implies variable water loss in the earthen fishponds; hence, we recommend characterizing these variations using non-invasive geophysical methods before establishing medium to large-scale earthen fishponds in the area.

The Kimberlina synthetic multiphysics dataset for CO2 monitoring investigations

AbstractWe present a synthetic multi‐scale, multi‐physics dataset constructed from the Kimberlina 1.2 CO2 reservoir model based on a potential CO2 storage site in the Southern San Joaquin Basin of California. Among 300 models, one selected reservoir‐simulation scenario produces hydrologic‐state models at the onset and after 20 years of CO2 injection. Subsequently, these models were transformed into geophysical properties, including P‐ and S‐wave seismic velocities, saturated density where the saturating fluid can be a combination of brine and supercritical CO2, and electrical resistivity using established empirical petrophysical relationships. From these 3D distributions of geophysical properties, we have generated synthetic time‐lapse seismic, gravity and electromagnetic responses with acquisition geometries that mimic realistic monitoring surveys and are achievable in actual field situations. We have also created a series of synthetic well logs of CO2 saturation, acoustic velocity, density and induction resistivity in the injection well and three monitoring wells. These were constructed by combining the low‐frequency trend of the geophysical models with the high‐frequency variations of actual well logs collected at the potential storage site. In addition, to better calibrate our datasets, measurements of permeability and pore connectivity have been made on cores of Vedder Sandstone, which forms the primary reservoir unit. These measurements provide the range of scales in the otherwise synthetic dataset to be as close to a real‐world situation as possible. This dataset consisting of the reservoir models, geophysical models, simulated time‐lapse geophysical responses and well logs forms a multi‐scale, multi‐physics testbed for designing and testing geophysical CO2 monitoring systems as well as for imaging and characterization algorithms. The suite of numerical models and data have been made publicly available for downloading on the National Energy Technology Laboratory's (NETL) Energy Data Exchange (EDX) website.

A model of transmissivity and hydraulic conductivity from electrical resistivity distribution derived from airborne electromagnetic surveys of the Mississippi River Valley Alluvial Aquifer, Midwest USA

Groundwater-flow models require the spatial distribution of the hydraulic conductivity parameter. One approach to defining this spatial distribution in groundwater-flow model grids is to map the electrical resistivity distribution by airborne electromagnetic (AEM) survey and establish a petrophysical relation between mean resistivity calculated as a nonlinear function of the resistivity layering and thicknesses of the layers and aquifer transmissivity compiled from historical aquifer tests completed within the AEM survey area. The petrophysical relation is used to transform AEM resistivity to transmissivity and to hydraulic conductivity over areas where the saturated thickness of the aquifer is known. The US Geological Survey applied this approach to a gain better understanding of the aquifer properties of the Mississippi River Valley alluvial aquifer. Alluvial-aquifer transmissivity data, compiled from 160 historical aquifer tests in the Mississippi Alluvial Plain (MAP), were correlated to mean resistivity calculated from 16,816 line-kilometers (km) of inverted resistivity soundings produced from a frequency-domain AEM survey of 95,000 km2 of the MAP. Correlated data were used to define petrophysical relations between transmissivity and mean resistivity by omitting from the correlations the aquifer-test and AEM sounding data that were separated by distances greater than 1 km and manually calibrating the relation coefficients to slug-test data. The petrophysical relation yielding the minimum residual error between simulated and slug-test data was applied to 2,364 line-km of AEM soundings in the 1,000-km2 Shellmound (Mississippi) study area to calculate hydraulic property distributions of the alluvial aquifer for use in future groundwater-flow models.

Petrophysical substantiation of geological section elastic-velocity property recovery potential based on TEM data

The geological section of Eastern and Western Siberia (Russia) is a very complicated object for seismic exploration. The research presented in the article is aimed at studying a petrophysical relation between electrical resistivity and P-wave velocity, as a basis for predicting the velocity model of the upper part of the section based on TEM sounding. Having performed a numerical modeling of petrophysical function, the authors calculated the dependency curves of electrical resistivity on the P-wave velocity. The obtained results of mathematical modeling and field experiments have proved the effectiveness of the proposed methodology as it increases the accuracy of geological model construction and enhances prediction reliability. Based on the dependences obtained, conclusions were drawn about the geological conditions favorable for a steady transition of section geoelectric characteristics to the velocity ones. The proposed technology is shown to provide a reliable reconstruction of the velocity model of the upper part of the section. The use of the developed methodology allows to improve the quality of seismic data processing and increase the accuracy of geological section boundary mapping at minimum cost based on the nature of the problem under investigation.

Updating the petrophysical model of a thin-layered clay reservoir, taking into account the identified lithofacies features

The article presents a study on the establishment of geological and facies conditions for the formation of a sandy-silty-clayey reservoir based on core analysis and linking the information obtained with neighboring areas. Previously used petrophysical relationships were generalized with underlying objects composed of thick monolithic sandstones and did not allow correctly assessing the properties of the reservoir under consideration. The work made it possible to clarify the idea of lithofacies features to substantiate their own petrophysical dependencies, which make it possible to differentiate thin-layered clay reservoirs in the section and determine their properties. The results obtained during the study made it possible to refine the petrophysical model, previously generalized to all the layers of the group. The revision of the boundary values and dependencies helped to justify the increase in reservoir oil reserves and became the basis for subsequent work to increase the profitability of putting the facility into commercial development.