Electromagnetic Resistivity Data Research Articles

Real-Time Rock-Properties Estimation for Geosteering: Statistical Rock-Physics-Driven Inversion of Seismic Acoustic Impedance and LWD Ultradeep Azimuthal Resistivity

Summary This work describes a statistical rock-physics-driven inversion of seismic acoustic impedance (AI) and ultradeep azimuthal resistivity (UDAR) log data, acquired while drilling, to estimate porosity, water saturation, and facies classes around the wellbore. Despite their limited resolution, seismic data integrated with electromagnetic resistivity log measurements improve the description of rock properties by considering the coupled effects of pore space and fluid saturation in the joint acoustic and electrical domains. The proposed inversion does not explicitly use a forward model, rather the correlation between the petrophysical properties and the resulting geophysical responses is inferred probabilistically from a training data set. The training set is generated by combining available borehole information with a statistical rock-physics modeling approach. In the inversion process, given colocated measurements of seismic AI and logging-while-drilling (LWD) electromagnetic resistivity data, the pointwise probability distribution of rock properties is derived directly from the training data set by applying the kernel density estimation (KDE) algorithm. A nonparametric statistical approach is used to approximate nonsymmetric volumetric distributions of petrophysical properties and to consider the characteristic nonlinear relationship linking water saturation with resistivity. Given an a priori facies classification template for the samples in the training set, it is possible to model the multimodal, facies-dependent behavior of the petrophysical properties, together with their distinctive correlation patterns. A facies-dependent parameterization allows the effect of lithology on acoustic and resistivity responses to be implicitly considered, even though the target properties of inversion are only porosity and saturation. To provide a realistic uncertainty quantification of the estimated rock properties, a plain Bayesian framework is described to account for rock-physics modeling error and to propagate seismic and resistivity data uncertainties to the inversion results. In this respect, the uncertainty related to the scale difference among the well-log data and seismic is addressed by adopting a scale reconciliation strategy. The main feature of the described inversion lies in its fast implementation based on a look-up table that allows rock properties, with their associated uncertainty, to be estimated in real time following the acquisition and inversion of UDAR data. This gives a robust, straightforward, and fast approach that can be effortlessly integrated into existing workflows to support geosteering operations. The inversion is validated on a clastic oil-bearing reservoir, where geosteering was used to guide the placement of a horizontal appraisal well in a complex structural setting. The results show that the proposed methodology provides realistic estimates of the rock-property distributions around the wellbore to depths of investigation of 50 m. These constitute useful information to drive geosteering decisions and can also be used, post-drilling, to update or optimize existing reservoir models.

Application of Data-Driven Discovery Machine Learning Algorithms in Predicting Geothermal Reservoir Temperature from Geophysical Resistivity Method

Geophysical methods including seismology, resistivity, gravity, magnetic and electromagnetic have been put in use for geothermal resource mapping at the Great Olkaria Geothermal field for decades. Reservoir temperature distribution and the electrical conductivity of rocks mainly depend on the same parameters such permeability, porosity, fluid salinity and temperature. This research focused on the integration of Olkaria Domes geothermal well testing temperature and geophysical Electromagnetic resistivity data with the aim of establishing an alternative estimation method for temperature of the reservoir through machine learning Analytics. To achieve this, Data-Driven Discovery Predictive Model Algorithm was built using Python programming language on Anaconda framework. The open-source web based application Jupyter Notebook for coding and visualization was used. Decision Tree Regression, Adaptive Booster Regression, Support Vector Regression and Random Forest Regression were used. The model performance was evaluated using R-Score and Mean Absolute Error metrics. Based on these performance score, the best performing model was suggested to predict subsurface temperature from resistivity. Training the model using the DTR algorithm approach provides superior outputs with R2 of 0.81 and lowest MAE of 29.8. The DTR algorithm could be implemented in determination of subsurface Temperature from resistivity in high temperature hydrothermal fields.

Validation of an Index for Susceptibility to Earthquake-Induced Landslides Derived from Helicopter-Borne Electromagnetic Resistivity and Digital Elevation Data

Catastrophic earthquake-induced landslides can occur on slopes composed of loosened and fractured rock masses. Although it is impossible to prevent such landslides, estimation of the susceptibility of slopes to earthquake-induced landslides is useful for risk management. An index of susceptibility to earthquake-induced landslides (ISEL) was developed by using helicopter-borne electromagnetic resistivity data. However, the ISEL has not yet been validated through the analysis of pre-earthquake data. In this study, ISEL values were estimated from resistivity and digital elevation data obtained in 2013 around Mt. Aso, Kyushu, before the 2016 Kumamoto earthquake. Although most of the landslides around Mt. Aso during the 2016 Kumamoto earthquake were mass movements of volcanic tephra layers, some of them occurred on loosened rock masses. Landslide susceptible areas at loosened rock masses are the target for ISEL value estimation. Our results validated the effectiveness of the ISEL as a predictor of earthquake-induced rock mass landslides.

Simulation–Regression Approximations for Value of Information Analysis of Geophysical Data

Value of information analysis is useful for helping a decision maker evaluate the benefits of acquiring or processing additional data. Such analysis is particularly beneficial in the petroleum industry, where information gathering is costly and time-consuming. Furthermore, there are often abundant opportunities for discovering creative information gathering schemes, involving the type and location of geophysical measurements. A consistent evaluation of such data requires spatial modeling that realistically captures the various aspects of the decision situation: the uncertain reservoir variables, the alternatives and the geophysical data under consideration. The computational tasks of value of information analysis can be daunting in such spatial decision situations; in this paper, a regression-based approximation approach is presented. The approach involves Monte Carlo simulation of data followed by linear regression to fit the conditional expectation expression that is needed for value of information analysis. Efficient approximations allow practical value of information analysis for the spatial decision situations that are typically encountered in petroleum reservoir evaluation. Applications are presented for seismic amplitude data and electromagnetic resistivity data, where one example includes multi-phase fluid flow simulations.

Porosity estimation at depths below the borehole bottom from resistivity logs and electromagnetic resistivity

ABSTRACTA neural network approach to porosity prediction below the well bottom from either the electrical resistivity logs or electromagnetic resistivity profiles is developed using the data from Soultz‐sous‐Forets area (France). It is shown that the neural network approach enables the porosity to be predicted at the depths below the bottom of the borehole from the resistivity well‐logging data and by resistivity profiles determined by inversion of the electromagnetic sounding data collected in the vicinity of the well. The results indicate that the forecasts based on the electrical logging data are more accurate (average relative errors being equal to 3%–5%) when the target depths do not exceed the double length of the well log used for calibration. In the absence of the resistivity logs at target depths or when the target to well depth ratio is large enough, the forecasts based on the electromagnetic resistivity data are more preferable.

A method for regionally mapping gravitationally deformed and loosened slopes using helicopter-borne electromagnetic resistivity data

Earthquake-induced deep-seated landslides are prone to occur at gravitationally deformed and loosened slopes. These slopes need to be identified for landslide susceptibility mapping and landslide risk assessment. Surface morphology is an effective factor for finding the slopes, but is not effective for estimating the degree and the area of loosening. Subsurface geophysical investigations are quite useful for determining subsurface geological structures in regions where loosening has occurred. In this study, a method was developed for regionally mapping gravitationally deformed and loosened slopes as earthquake-induced deep-seated landslide susceptible zones by differentiating from sound bedrocks in flexural toppled slopes. Using the helicopter-borne electromagnetic resistivity data, “average ruggedness of resistivity” was proposed as an index parameter for differentiating the loosened zones. The estimated loosened zones were compared with observed geomorphological features during field survey, and it is shown that “average ruggedness of resistivity” is a useful parameter for regional mapping of earthquake-induced deep-seated landslide susceptible slopes.

Extending Airborne Electromagnetic Surveys for Regional Active Layer and Permafrost Mapping with Remote Sensing and Ancillary Data, Yukon Flats Ecoregion, Central Alaska

Machine‐learning regression tree models were used to extrapolate airborne electromagnetic resistivity data collected along flight lines in the Yukon Flats Ecoregion, central Alaska, for regional mapping of permafrost. This method of extrapolation (r = 0.86) used subsurface resistivity, Landsat Thematic Mapper (TM) at‐sensor reflectance, thermal, TM‐derived spectral indices, digital elevation models and other relevant spatial data to estimate near‐surface (0–2.6‐m depth) resistivity at 30‐m resolution. A piecewise regression model (r = 0.82) and a presence/absence decision tree classification (accuracy of 87%) were used to estimate active‐layer thickness (ALT) (< 101 cm) and the probability of near‐surface (up to 123‐cm depth) permafrost occurrence from field data, modelled near‐surface (0–2.6 m) resistivity, and other relevant remote sensing and map data. At site scale, the predicted ALTs were similar to those previously observed for different vegetation types. At the landscape scale, the predicted ALTs tended to be thinner on higher‐elevation loess deposits than on low‐lying alluvial and sand sheet deposits of the Yukon Flats. The ALT and permafrost maps provide a baseline for future permafrost monitoring, serve as inputs for modelling hydrological and carbon cycles at local to regional scales, and offer insight into the ALT response to fire and thaw processes. Published 2013. This article is a U.S. Government work and is in the public domain in the USA.

Cross-property rock physics relations for estimating low-frequency seismic impedance trends from electromagnetic resistivity data

Estimating the low-frequency seismic impedance trend is critical for reservoir characterization based on seismic inversions. Conventional seismic reflection data have an information gap between ∼2 and 10 Hz and do not contain information about the background impedance trend. On the other hand, controlled source electromagnetic (CSEM) data are typically low resolution, and contain information about the Earth's resistivity structure. Thus, cross-property rock physics relations between elastic and electrical properties can extract information from the CSEM data to provide constraints on the low-frequency seismic impedance trend. This concept is tested with well-log data and applied to a resistivity section inverted from CSEM data. Results show that appropriate cross-property rock physics relations can capture the background low-frequency impedance trend and provide useful constraints on models for inversion.

Geophysical methods as mapping tools in a strata-bound gold deposit; Haile Mine, South Carolina slate belt

Very low frequency electromagnetic, magnetic, and induced polarization methods. The very low frequency electromagnetic resistivity data show high resistivity zones that correlate with siliceous units known to host the gold. The magnetic data clearly identify the mafic and ultramafic dikes which cut metasediments and also fill faults that offset them. The induced polarization method allows examination of the mineralized metasediments in two dimensions by giving vertical sections of resistivity and percent frequency effect, parameters which correlate with silica and pyrite content, respectively. Lithologic units as well as ore zones can thus be mapped in this terrain which is deeply weathered and partially covered by coastal plain sediments.--Modified journal abstract.