Fast Bayesian Inversion Method for the Generalized Petrophysical and Compositional
            Interpretation of Multiple Well Logs With Uncertainty Quantification

Deng, Tianqi; Ambía, Joaquín; Torres‐Verdín, Carlos

doi:10.30632/t60als-2019_ffff

Cited by 14 publications

(10 citation statements)

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“…First and foremost we verify the method on deep EM measurements. When the positions of the boundaries are assumed known, our interpretation is qualitatively similar to earlier work: estimation is less certain in geological configurations with thin layers and high contrast between each layer's petrophysical properties [Deng et al, 2019]. Furthermore, the presented results indicate that the method is capable of reducing boundary and property uncertainties within the expected range of sensitivities of the modeled deep EM tool: with low uncertainties when the boundary is within 15 m from the tool [Larsen et al, 2019, ].…”

Section: Discussionsupporting

confidence: 76%

“…This means that the total number of forward runs is as low as 3×40 = 120, compare to 10000 needed for the MCMC. Figure 3 shows that by decreasing layer thicknesses, the value of the posterior standard deviation increases; as expected because of the higher relative impact of shoulder bed effects [Deng et al, 2019, ].…”

Section: Bulk Density Estimationsupporting

confidence: 59%

“…A comprehensive albeit brute-force approach, like the Metropolis-Hastings method, will require hundreds of thousands of forwards runs to establish a solution [Metropolis et al, 1953, Bottero et al, 2016. To mitigate this problem, clever ways to reduce the sampling space, without reducing the accuracy of the final solution, have been proposed: [Shen et al, 2017] implemented a Hamiltonian Monte Carlo (HMC) method, and [Deng et al, 2019] proposed a Bayesian inference approach. These approaches could be applied in a geosteering time constrained setting, however they consider the location of the boundaries (between the geological layers) a known input.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Ensemble-Based Well Log Interpretation and Uncertainty Quantification for Geosteering

Jahani¹,

Garrido²,

Alyaev³

et al. 2021

Preprint

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The costs for drilling offshore wells are high and hydrocarbons are often located in complex reservoir formations. To effectively produce from such reservoirs and reduce costs, optimized well placement in real-time (geosteering) is crucial. Geosteering is usually assisted by an updated formation evaluation obtained by well-log interpretation while drilling. A reliable, computationally efficient, and robust workflow to interpret well logs and capture uncertainties in real-time is necessary for this application. An iterative ensemble-based method, namely the approximate Levenberg Marquardt form of the Ensemble Randomized Maximum Likelihood (LM-EnRML) is integrated in our formation evaluation workflow. We estimate model parameters, resistivity and density in addition to boundary locations, and related uncertainties by reducing the statistical misfit between the measurements from the well logging tools and the theoretical measurements from the forward tool simulators. The results of analyzing several synthetic cases with several types of logs verified that the proposed method can give good estimate of model parameters by employing as few as 40 ensemble members and 2-10 iterations. By comparing the CPU time, we conclude that the proposed method has at least about 10-125 times lower computational time compare to a common statistical method, such as Metropolis-Hastings Monte Carlo. In addition, the ensemble-based method can run in parallel on multiple CPUs. The reliability and speed of well-log interpretation is normally sensitive to several parameters such as the distances between the formation boundaries and the logging tool, model parameter's contrast, formation layer thickness and well inclination. Testing the method on a case inspired from a real field also yielded accurate formation evaluation. Thus, the proposed ensemble-based method has been proven robust and computationally efficient to estimate petrophysical formation properties, layer boundaries and their uncertainties, indicating that it is suitable for geosteering.

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Section: Discussionsupporting

confidence: 76%

Section: Bulk Density Estimationsupporting

confidence: 59%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Ensemble-Based Well Log Interpretation and Uncertainty Quantification for Geosteering

Jahani¹,

Garrido²,

Alyaev³

et al. 2021

Preprint

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show abstract

“…21 The combination of the quasi-Newton methods and Markov Chain Monte Carlo sampling methods (QNMCMC) was used to improve the accuracy of joint inversion and evaluate formation properties. 22 However, the methods mentioned above did not consider the effects of high temperature and high pressure on the inversion process, and they were not used to evaluate CO 2 saturation level.…”

Section: Introductionmentioning

confidence: 99%

“…Nuclear magnetic resonance (NMR) logs were used in combination with logging parameters such as density, Sigma (neutron capture cross section), HI (hydrogen index), migration length, PEF, and neutron porosity measurements for joint inversion . The combination of the quasi-Newton methods and Markov Chain Monte Carlo sampling methods (QNMCMC) was used to improve the accuracy of joint inversion and evaluate formation properties . However, the methods mentioned above did not consider the effects of high temperature and high pressure on the inversion process, and they were not used to evaluate CO 2 saturation level.…”

Section: Introductionmentioning

confidence: 99%

CO₂-Containing Reservoir Evaluation Based on the Joint Inversion of Nuclear Logs and Resistivity Logs: A Case Study of Ultrahigh-Temperature and High-Pressure Gas Reservoirs in the Yinggehai Basin, China

Zhang

Xiao

et al. 2022

ACS Omega

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Due to the unique characteristics of reservoirs in the Yinggehai Basin in the South China Sea, such as high temperature and high pressure (HPHT), low porosity, low permeability, complex pore structure, and high lime content, the log responses of these reservoirs have very complex characteristics, which makes it difficult to evaluate reservoir parameters accurately. In addition, most reservoirs in Ledong Block of the Yinggehai Basin in the South China Sea contain CO 2 , posing great difficulties for subsequent exploration and development. Accurate evaluation of CO 2 layers is of paramount importance for the development of oil and gas fields. In this study, we used a method for the joint inversion of multiple well logs to evaluate the reservoirs and determine CO 2 saturation level and other formation parameters. We optimized the joint inversion model based on the characteristics of the reservoirs in the Yinggehai Basin and adjusted the forward simulation model to consider the effects of high temperature and high pressure on gas density. In view of high lime content in the formations in this area, we adjusted the resistivity forward simulation model to consider the effect of lime content. The inversion results show that the values of porosity, permeability, and water saturation level obtained through inversion are largely consistent with the core data. The CO 2 saturation level determined through joint inversion is 22%, which represents a deviation of less than 10% from the drilling system testing (DST) result, indicating that the joint inversion method is accurate. The error in the water saturation level determined through the joint inversion method is smaller than that in the calculated results from conventional multimineral inversion models. We performed forward simulation of the results calculated with the joint inversion method and compared the results of forward simulation with actual log curves. For the sandstone interval, the results of forward simulation are largely consistent with the actual log curves, indicating that the joint inversion method is accurate. In summary, the method presented in this paper can accurately determine reservoir parameters and provide strong support for the exploration and development of oil and gas fields in the Yinggehai Basin in the South China Sea.

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Rock Mineral Volume Inversion Using Statistical and Machine Learning Algorithms for Enhanced Geothermal Systems in Upper Rhine Graben, Eastern France

Joshua,

Marquis,

Maurer

et al. 2024

Journal of Geophysical Research: Machine Learning and Computati

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Accurately determining the mineralogical composition of rocks is essential for precise assessments of key petrophysical properties like effective porosity, water saturation, clay volume, and permeability. Mineral volume inversion is particularly critical in geological contexts characterized by heterogeneity, such as in the Upper Rhine Graben (URG), where both carbonate and siliciclastic formations are prevalent. The estimation of mineral volumes poses challenges that involve both linear and nonlinear relationships associated with geophysical data. To address this complexity, our methodology strategically integrates the robust insights from standard statistical approaches with three machine learning (ML) algorithms: multi‐layer perceptron, random forest regression, and gradient boosting regression. Furthermore, we propose a new hybrid ensemble model that incorporates a weighted average of multiple ML approaches to predict mineral composition within the Muschelkalk and Buntsandstein formations of the URG. ML techniques for mineral composition prediction in these formations exhibit robust predictive performance. The predicted mineral volumes align closely with quantitative estimates derived from X‐ray diffraction analysis. Additionally, they are in good qualitative agreement with mineral descriptions obtained from cores and cuttings of the Muschelkalk and Buntsandstein formations.

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Fast Bayesian Inversion Method for the Generalized Petrophysical and Compositional Interpretation of Multiple Well Logs With Uncertainty Quantification

Cited by 14 publications

References 0 publications

Ensemble-Based Well Log Interpretation and Uncertainty Quantification for Geosteering

Ensemble-Based Well Log Interpretation and Uncertainty Quantification for Geosteering

CO₂-Containing Reservoir Evaluation Based on the Joint Inversion of Nuclear Logs and Resistivity Logs: A Case Study of Ultrahigh-Temperature and High-Pressure Gas Reservoirs in the Yinggehai Basin, China

Rock Mineral Volume Inversion Using Statistical and Machine Learning Algorithms for Enhanced Geothermal Systems in Upper Rhine Graben, Eastern France

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Fast Bayesian Inversion Method for the Generalized Petrophysical and Compositional Interpretation of Multiple Well Logs With Uncertainty Quantification

Cited by 14 publications

References 0 publications

Ensemble-Based Well Log Interpretation and Uncertainty Quantification for Geosteering

Ensemble-Based Well Log Interpretation and Uncertainty Quantification for Geosteering

CO2-Containing Reservoir Evaluation Based on the Joint Inversion of Nuclear Logs and Resistivity Logs: A Case Study of Ultrahigh-Temperature and High-Pressure Gas Reservoirs in the Yinggehai Basin, China

Rock Mineral Volume Inversion Using Statistical and Machine Learning Algorithms for Enhanced Geothermal Systems in Upper Rhine Graben, Eastern France

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CO₂-Containing Reservoir Evaluation Based on the Joint Inversion of Nuclear Logs and Resistivity Logs: A Case Study of Ultrahigh-Temperature and High-Pressure Gas Reservoirs in the Yinggehai Basin, China