The offshore EM challenge

Hesthammer, Jonny; Boulaenko, Mikhail

doi:10.3997/1365-2397.23.1089.26739

Cited by 25 publications

(3 citation statements)

References 14 publications

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“…In Equation 5, a logarithmic identifier is used to simplify the integral expression involved in the marginal likelihood method [25]. Once the hyperparameters are successfully estimated, the estimation of the GP regression is then defined using Equations (2) and (3). The m * is a mean value of the estimation that gives the best estimate of y * (i.e., magnitude of E-field at x * ), and the κ * is the predictive variance of the testing input in terms of two standard deviations (95% confidence interval), which reflects the reliability of the predictive mean.…”

Section: Evaluating Electromagnetic Profile Using the Gaussian Processmentioning

confidence: 99%

“…The main component of an SBL is the utilization of a towed EM source known as a horizontal electric dipole (HED) and stationary EM receivers for recording the returned EM signals [1]. Studies related to the commercial SBL application can be found in [1][2][3][4][5][6][7][8][9]. Figure 1 is adopted from [10] to depict the illustration of the SBL application in the offshore environment.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Methodology for Hydrocarbon Depth Prediction in Seabed Logging: Gaussian Process-Based Inverse Modeling of Electromagnetic Data

et al. 2021

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Seabed logging (SBL) is an application of electromagnetic (EM) waves for detecting potential marine hydrocarbon-saturated reservoirs reliant on a source–receiver system. One of the concerns in modeling and inversion of the EM data is associated with the need for realistic representation of complex geo-electrical models. Concurrently, the corresponding algorithms of forward modeling should be robustly efficient with low computational effort for repeated use of the inversion. This work proposes a new inversion methodology which consists of two frameworks, namely Gaussian process (GP), which allows a greater flexibility in modeling a variety of EM responses, and gradient descent (GD) for finding the best minimizer (i.e., hydrocarbon depth). Computer simulation technology (CST), which uses finite element (FE), was exploited to generate prior EM responses for the GP to evaluate EM profiles at “untried” depths. Then, GD was used to minimize the mean squared error (MSE) where GP acts as its forward model. Acquiring EM responses using mesh-based algorithms is a time-consuming task. Thus, this work compared the time taken by the CST and GP in evaluating the EM profiles. For the accuracy and performance, the GP model was compared with EM responses modeled by the FE, and percentage error between the estimate and “untried” computer input was calculated. The results indicate that GP-based inverse modeling can efficiently predict the hydrocarbon depth in the SBL.

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Section: Evaluating Electromagnetic Profile Using the Gaussian Processmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Methodology for Hydrocarbon Depth Prediction in Seabed Logging: Gaussian Process-Based Inverse Modeling of Electromagnetic Data

et al. 2021

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“…The property of the electrical resistivity (inversely proportional to conductivity) plays an important role for the hydrocarbon exploration. Main references of the SBL can be found in References [1][2][3][4][5][6][7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Stochastic Process-Based Inversion of Electromagnetic Data for Hydrocarbon Resistivity Estimation in Seabed Logging

et al. 2021

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This work proposes a stochastic process-based inversion to estimate hydrocarbon resistivity based on multifrequency electromagnetic (EM) data. Currently, mesh-based algorithms are used for processing the EM responses which cause high time-consuming and unable to quantify uncertainty. Gaussian process (GP) is utilized as the alternative forward modeling approach to evaluate the EM profiles with uncertainty quantification. For the optimization, gradient descent is used to find the optimum by minimizing its loss function. The prior EM profiles are evaluated using finite element (FE) through computer simulation technology (CST) software. For validation purposes, mean squared deviation and its root between EM profiles evaluated by the GP and FE at the unobserved resistivities are computed. Time taken for the GP and CST to evaluate the EM profiles is compared, and absolute error between the estimate and its simulation input is also computed. All the resulting deviations were significantly small, and the GP took lesser time to evaluate the EM profiles compared to the software. The observational datasets also lied within the 95% confidence interval (CI) where the resistivity inputs were estimated by the proposed inversion. This indicates the stochastic process-based inversion can effectively estimate the hydrocarbon resistivity in the seabed logging.

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Geophysical Surveys and Data Analysis

Thakur

Rajput

2010

Exploration of Gas Hydrates

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The offshore EM challenge

Cited by 25 publications

References 14 publications

A Novel Methodology for Hydrocarbon Depth Prediction in Seabed Logging: Gaussian Process-Based Inverse Modeling of Electromagnetic Data

A Novel Methodology for Hydrocarbon Depth Prediction in Seabed Logging: Gaussian Process-Based Inverse Modeling of Electromagnetic Data

Stochastic Process-Based Inversion of Electromagnetic Data for Hydrocarbon Resistivity Estimation in Seabed Logging

Geophysical Surveys and Data Analysis

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