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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Improving multiwell petrophysical interpretation from well logs via machine learning and statistical models

Well-log interpretation estimates in situ rock properties along well trajectory, such as porosity, water saturation, and permeability, to support reserve-volume estimation, production forecasts, and decision making in reservoir development. However, due to measurement errors, variability of well logs caused by multiple measurement vendors, different borehole tools, and nonuniform drilling/borehole conditions, estimations of rock properties with original well logs without proper preprocessing may not be accurate, especially in the context of multiwell estimation. Well-log normalization techniques such as two-point scaling and mean-variance normalization are commonly used to improve the robustness of multiwell rock-property estimation. However, these techniques do not consider the correlation between well logs and require subjective knowledge for their effective implementation. To reduce uncertainties and processing time associated with multiwell rock-property estimation from well logs, we develop discriminative adversarial (DA) and linear constraint models for well-log normalization and rock-property estimation. The DA neural network model developed for well-log normalization and interpretation can perform linear and nonlinear well-log normalization while considering the joint distribution of each well log and rock properties. However, the linear constraint model uses an ensemble of predictions from linear models to constrain well-log normalization and rock-property estimation. We also develop a divergence-based type well identification method to select type (training) wells for a test well based on the statistical similarity of associated well-log distributions instead of the interwell distance. We apply the DA model to perform well-log normalization and prediction of permeability for the Seminole San Andres Unit carbonate reservoir. Compared with the permeability predicted with the classical machine learning model without well-log normalization and models with two-point scaling normalization, the DA model yields the most accurate permeability prediction by decreasing the mean-squared error of permeability prediction by 20%–50%.

Geochemistry & Geophysics↗
DOE OSTI2025

Machine Learning for Well Log Analysis in Uranium Mining

This project explores the use of Artificial Intelligence (AI) and Machine Learning (ML) techniques to automate well log analysis for uranium mining. Geophysical log data—spontaneous potential, resistivity, and gamma ray—were used to classify lithology, correlate well logs and identify roll front zonation patterns, which are critical for locating uranium ore bodies. Supervised ML algorithms such as eXtreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Random Forest were trained to classify lithology with high accuracy. Gradient Boosting Machines (GBM), XGBoost, Random Forest, and Neural Networks were also used for role front zone identification. Moreover, a Fast Dynamic Time Warping (FastDTW) algorithm was employed for well log correlation. Additionally, sample lag was addressed using dynamic programming. Results demonstrate the potential of AI and ML to streamline well log analysis and enhance uranium exploration workflows.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

CarbonSafe II Project: Dryfork Station UW PRB 1 Well Core, Fluid, and Well Logs datasets

This dataset contains the raw data sets of thee UW PRB 1 well's geophysical well logs, core measurements and fluid analysis for the CarbonSafe II project at the Dryfork Station in Gillette, WY. Focus was placed on the Lakota, Hulett and Minnelusa Formations and their overlying seals. Also included are well logs from legacy wells from the surrounding area.

CarbonSafe 2↗
NASA NTRS1992

Application of Nuclear Well Logging Techniques to Lunar Resource Assessment

The use of neutron and gamma ray measurements for the analysis of material composition has become well established in the last 40 years. Schlumberger has pioneered the use of this technology for logging wells drilled to produce oil and gas, and for this purpose has developed neutron generators that allow measurements to be made in deep (5000 m) boreholes under adverse conditions. We also make ruggedized neutron and gamma ray detector packages that can be used to make reliable measurements on the drill collar of a rotating drill string while the well is being drilled, where the conditions are severe. Modern nuclear methods used in logging measure rock formation parameters like bulk density and porosity, fluid composition, and element abundances by weight including hydrogen concentration. The measurements are made with high precision and accuracy. These devices (well logging sondes) share many of the design criteria required for remote sensing in space; they must be small, light, rugged, and able to perform reliably under adverse conditions. We see a role for the adaptation of this technology to lunar or planetary resource assessment missions.

Albats, P.↗
DOE OSTI2022

General Overview of Mobile Sources Used for Well Logging and Industrial Radiography Applications

Mobile sources is a term most commonly used to describe radioactive sources that are used in applications requiring frequent transportation. Such radioactive sources are in common use world-wide where typical applications include radiographic non-destructive evaluation (NDE) and oil and gas well logging, among others requiring lesser amounts of radioactivity. This report provides a general overview of mobile sources used for well logging and industrial radiography applications including radionuclides used, equipment, and alternative technologies. Information presented here has been extracted from a larger study on common mobile radiation sources and their use.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Integrating Experiments and Well Logs to Predict Caney Shale Static Mechanical Properties During Production with Supervised Machine Learning

Caney shale is one of the emerging oil reservoirs in Oklahoma. Understanding the impact of effective stress on its mechanical properties is critical for predicting hydraulic fracture geometry and overall hydrocarbon production. The objective of our study is to evaluate the impact of effective stress on the dynamic Young’s modulus using ultrasonic velocity measurements for Caney shale samples. A triaxial cell was utilized to measure ultrasonic (P-wave and S-wave) velocities for ten downhole Caney shale samples under various effective stresses to indirectly assess the impact of pore pressure change. The dynamic Young’s moduli estimated from these measurements were integrated with available conventional well logs (excluding sonic logs) and triaxial test results from Benge et al. (2021) to predict the static Young’s modulus using Random Forest (RF) and Extreme Gradient Boosting (XGBoost) models. The results showed that the estimated dynamic Young’s moduli from ultrasonic measurements were higher than the corresponding static Young’s modulus of cores from the same vertical well at similar depths. With increasing effective stress, the dynamic Young’s modulus increased for all samples. The estimated dynamic-to-static correction factor tended to be higher in zones of high neutron porosity (PHIN) and low density compared to other zones. Finally, SHapley Additive exPlanations (SHAP) for RF and XGBoost models identified depth, gamma ray (GR), and PHIN as key features for predicting the static Young’s modulus. This study enhances our understanding of the dynamic and static Young’s moduli for the Caney shale interval, as a function of effective stress and conventional well logs. The findings from this study can improve predictions of production throughout the well's lifespan by offering insights into the mechanical property degradation resulting from pore pressure depletion.

Kholy, Sherif M.↗
DOE OSTI2020

Bi-fidelity Gradient-Based Approach for Nonlinear Well Logging Inverse Problems

Solving a non-linear inverse problem is challenging in computational science and engineering. Sampling based methods require a large number of model valuations; gradientbased methods require fewer model evaluations but only find the local minima. Multifidelity optimization combines the low fidelity model and the high-fidelity model to achieve both high accuracy and high efficiency. In this paper, we present a bi-fidelity approach to solve non-linear inverse problems. In the bi-fidelity inversion method, the low-fidelity model is used to acquire a good initial guess, and the high-fidelity model is used to locate the global minimum. Combined with a multi-start optimization scheme, the proposed approach significantly increases the possibility of finding the global minimum for nonlinear inverse problems with many local minima. The method is tested with two toy problems and then applied to an electromagnetic well logging inverse problem, which is difficult to solve using traditional gradient-based methods. The bi-fidelity method provides promising inversion results and can be easily applied to traditional gradient-based methods.

42 ENGINEERING↗
DOE OSTI2020

Prediction of gas hydrate saturation using machine learning and optimal set of well-logs

We report resistivity and acoustic logs are widely used to estimate gas hydrate saturation in various sedimentary systems using one of the two popular methods ((1) acoustic velocity and (2) electrical resistivity), but the limitations of these two methods are often overlooked, which include (i) well-specific calibration of empirical exponents in the electrical resistivity method, (ii) assumption of known pore morphology for gas hydrates in the acoustic velocity method, and (iii) presence of unknown mineralogy and bulk modulus terms in the acoustic velocity method. NMR-density porosity-derived gas hydrate saturation based on the analysis of the transverse magnetization relaxation time (T2) is considered the most precise method, but acquisition of NMR-based logs is limited at relatively recent drilled sites; additionally, its use in conventional oil and gas reservoirs is not that common due to higher cost and operational deployment limitations associated with acquiring NMR well-logs. This study proposes a new method that predicts gas hydrate saturation (S h ) for any well using porosity, bulk density, and compressional wave (P wave) velocity well-logs with neural network (or stochastic gradient descent regression) without any well-specific calibration and/or other aforementioned shortcomings of the existing methods. The method is developed by examining the underlying dependency between S h and different combinations of well-logs, chosen from 6 routine logs, with 12 different machine learning (ML) algorithms. The accuracy of the proposed method in predicting S h is ~ 84%, which is better than the accuracy of seismic and electrical resistivity methods (≤ 75%) per the results reported by three different studies. The robustness of the method in the specific case of permafrost-associated gas hydrates is demonstrated with well-log data from two wells drilled on the Alaska North Slope.

58 GEOSCIENCES↗
DOE OSTI2023

From Well Log to Formation Model: A Novel Laboratory Calibrated Methodology with Demonstration

This work demonstrates how the characterization and modeling of both elastic and creep properties are essential to describe zones in layered rock formations as either low-stress targets for stimulation or high-stress barriers to fracture growth. Prediction of fracture height is critical for designing stimulation operations in oil and gas wells. Ideally, fractures are placed in target zones which will produce hydrocarbons and should not propagate into zones expected to be unproductive or to produce unwanted fluids such as water which in turn must be treated and/or disposed. The essential task in designing stimulation plans is predicting which zones have low horizontal stresses and which will be high-stress barriers to fracture growth. Despite this importance, there are gaps in current knowledge and a complete workflow from laboratory characterization to a finite element model which includes time dependent rock deformation is required. While the research and methodology presented here also have application to CO 2or hydrogen storage, wastewater injection, and geothermal applications, the focus will be on hydrocarbon extraction. This thesis presents the results of a characterization-to-prediction workflow for the Caney shale, which is an emerging hydrocarbon resource in Oklahoma, USA. It begins with an investigation to enable critical evaluation of the Caney zonation into nominally “brittle” and “ductile” zones based on properties observed from well logs. It shows none of the zones are consistently “brittle” or “ductile” mechanical behavior based on the variety of definitions of these terms. However, the nominally ductile zones are weaker and more prone to creep. A laboratory investigation of samples including strength, elastic, and creep properties, is then used in a finite element model of stress evolution. The model includes both elastic deformation and viscoplastic creep. Results predict the least creep-prone layers to have the lowest horizontal stresses, therefore comprising hydraulic fracturing targets. The most creep-prone layers attain a horizontal stress similar to the vertical stress and therefore are predicted to be high stress barriers to hydraulic fracture stimulation. In addition to defining stimulation target intervals, the model shows how as tectonic strain rate increases, there is a transition from creep-dominated stresses to stresses dominated by elasticity.

Benge, Margaret↗
DOE OSTI2024

Well-Log Derived Geomechanical Analysis of Microseismicity in the Mt. Simon Saline Aquifers (Illinois Basin - Decatur Project)

The Illinois Basin Decatur Project (IBDP) successfully demonstrated the safe geologic storage of carbon dioxide at a commercial scale. Within the IBDP project three deep wells (injection (CCS1), monitoring (VW1), geophysical (GM1)) were competed and geophysical logs were recorded. During injection and post-injection periods microseismic monitoring was conducted to create a miscoseismic catalog. The correlations between microseimic attributes and geomechanical well logs define major geomechanical drivers of microseismic expression to understand a reservoir response to CO2 injection in geological context. Utilizing standard sonic and density well logs, the dynamic elastic moduli were calculated and employed to correlate with microseismic pseudo-logs. A multi-dimensional Mu-rho and Lambda-rho (MRLR) hyperdimensional plots display of meaningful data and uncovered subtle relationships between elastic properties of sandstones and the seismological attributes of recorded microseismicity.

Myshakin, Evgeniy↗
DOE OSTI2020

Characterization of elastic mechanical properties of Tuscaloosa Marine Shale from well logs using the vertical transversely isotropic model

To avoid steep declines in the Tuscaloosa Marine Shale (TMS) production, wells are fracture-stimulated to release the hydrocarbons trapped in the matrix of the formation. An accurate estimation of Young’s modulus and Poisson’s ratio is essential for hydraulic fracture propagation. In addition, ignoring the highly heterogeneous and anisotropic character of TMS can lead to erroneous stress values, which subsequently affect hydraulic fracture width estimates and the overall hydraulic fracturing process. We have developed an empirical 1D geomechanical model that takes into account VTI anisotropy, and it is used to characterize the elastic mechanical properties of TMS in two wells. In the analyzed formation, the vertical Poisson’s ratio is less than the horizontal Poisson’s ratio, which suggests the necessity of an alternative to the ANNIE equations. The stiffness coefficients [Formula: see text] and [Formula: see text] were estimated using the relationships developed from the ultrasonic core data available for the two TMS. Further, correlations between the static and dynamic properties from laboratory tests were used to improve the minimum horizontal stress calculation. We compare VTI Young’s moduli, Poisson’s ratios, and minimum horizontal stress with the isotropic solution. VTI modeling improves the estimation of the elastic mechanical properties. The isotropic solution underestimates the minimum horizontal stress in the formation. Moreover, it was shown that the 20 ft shale interval below the TMS base is characterized by a low Young’s modulus (the vertical Young’s modulus is equal to 20 GPa, whereas the horizontal Young’s modulus is equal to 40 GPa) and may be a frac barrier.

Geochemistry & Geophysics↗
NASA NTRS1996

A Model of the Chicxulub Impact Basin Based on Evaluation of Geophysical Data, Well Logs, and Drill Core Samples

Abundant evidence now shows that the buried Chicxulub structure in northern Yucatan, Mexico, is indeed the intensely sought-after source of the ejecta found world-wide at the Cretaceous-Tertiary (K/T) boundary. In addition to large-scale concentric patterns in gravity and magnetic data over the structure, recent analyses of drill-core samples reveal a lithological assemblage similar to that observed at other terrestrial craters. This assemblage comprises suevite breccias, ejecta deposit breccias (Bunte Breccia equivalents), fine-grained impact melt rocks, and melt-matrix breccias. All these impact-produced lithologies contain diagnostic evidence of shock metamorphism, including planar deformation features in quartz, feldspar, and zircons; diaplectic glasses of quartz and feldspar; and fused mineral melts and whole-rock melts. In addition, elevated concentrations of Ir, Re, and Os, in meteoritic relative proportions, have been detected in some melt-rock samples from the center of the structure. Isotopic analyses, magnetization of melt-rock samples, and local stratigraphic constraints identify this crater as the source of K/T boundary deposits.

Sharpton, Virgil L.↗