Facies and Lithofacies Classifications from Well Logs

Z., Y.

doi:10.1007/978-3-030-17860-4_10

Cited by 6 publications

(4 citation statements)

References 14 publications

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“…To create the PEMs relationship, a petrophysical evaluation is carried out that quantifies the reservoir zone and provides critical parameters such as clay and calcite volumetrics, effective porosities (PHIE), and water saturation (Sw). The litho-facies classification depends on the well-log based estimates of petrophysical properties computed in the formation evaluation analysis, and the cut-off method is used to discriminate between facies utilizing interpreted logs confirmed with well-perforations (Lindberg, 2015;Ma, 2019). The identified lithologies based on the petrophysical cut-off values are listed in Table 1 and are employed along with reservoir properties in PEMs to accurately estimate their elastic responses.…”

Section: Methodsmentioning

confidence: 99%

Bayesian stochastic inversion with petro-elastic relation to quantify thin gas sands of Khadro Formation, Zamzama gas field

et al. 2022

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A challenging situation in reservoir characterization arises when reservoirs occur below the vertical resolution of seismic, such as the 6 m gas-prone sands of the heterogenous Khadro Formation. This formation is the Zamzama gas field's secondary reservoir and needs detailed evaluation for optimum production of the field as the deeper primary reservoir of Pab sandstone is facing the problem of early water encroachment in its producing wells. The characterization of thin heterogeneous gas-bearing facies is hampered by the insensitivity of well-bore recording tools in precisely capturing elastic properties. Consequently, petro-elastic models (PEMs) of identified facies are generated that produce consistent elastic responses and discriminate facies in their true elastic domains. The resolution of seismic elastic properties is enhanced to illuminate the thin gas-bearing sands by adopting a Bayesian stochastic inversion process in collaboration with PEMs modeled elastic responses on a fine-scale stratigraphic grid. In addition, the Bayesian framework is used to estimate the litho-facies probability cubes to mitigate the drilling risks by integrating highly sampled posterior elastic volumes, modeled elastic logs, and geologic information of identified litho-facies. The outcomes included prospect identification at the drilled well locations that need to be perforated, while new potential zones are present for additional wells.

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Section: Methodsmentioning

confidence: 99%

Bayesian stochastic inversion with petro-elastic relation to quantify thin gas sands of Khadro Formation, Zamzama gas field

et al. 2022

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“…Consequently, the preparation of training data holds crucial significance. According to the works of Majid, Hung, Danial, et al (2022); Majid, Hung, Zhenxue, et al (2022) and Ma (2019), there has been a growing trend in recent decades toward the quantitative application of seismic attributes in the seismic characterization of reservoirs. Overall, seismic attributes refer to measurements, derived from seismic records to determine physical correlations between seismic data and geological properties.…”

Section: Developing Baseline Modelsmentioning

confidence: 99%

Ensemble of Neural Networks Utilizing Seismic Attributes for Rock‐Property Inversion With Uncertainty Estimation

Ntibahanana,

Jianguo,

Luemba

et al. 2024

Earth and Space Science

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Seismic inversion holds significant importance across various domains of geoscience and engineering, including the characterization of energy resource reservoirs, the assessment of polluted sites, and CO2 storage. It is a process of estimating rock properties from seismic data that is inherently uncertain, nonlinear, non‐unique, and highly challenging. Using multiple seismic attributes increases the size of the data, requiring considerable processing resources and time. However, deep learning can accurately fit quantities of nonlinear variables, making it an excellent method for predicting spatially distributed subsurface properties. We trained some multi‐output regression neural networks to carry out porosity inversion from seismic data. We initially computed a series of seismic attributes and generated the corresponding porosity using interpreted horizons, well logs, and seismic data. Subsequently, we proposed a technique to identify the most relevant seismic attributes for porosity inversion. Because our networks work as stochastic modeling entities, we created a weight‐averaging ensemble approach to build a strong model with the highest level of accuracy. We combined realizations from baseline entities, considering their respective performance levels. Using the statistics between these realizations and the robust model, we determined the degree of uncertainty associated with the outcome. We found an R2 of 0.993 and an MAE of 0.00112 in the F3 block offshore the Netherlands, proving the method's effectiveness. The mean porosity was 0.175193, compared to 0.175626 from a reference model, and the mean uncertainty was ±0.0008998.

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“…|e i | n samples (7) The root mean squared error (RMSE), as shown by the following equation, is a quadratic scoring rule that is used to square the errors before they are averaged. For comparison with the MAE, the RMSE gives high weight to large errors.…”

Section: Mae Y ŷ Metric = N Samples I=1mentioning

confidence: 99%

“…In addition, porosity is key in the assessment of subsurface contamination [4] and CO 2 sequestration [5]. Commonly, a variety of techniques are used to determine the The associate editor coordinating the review of this manuscript and approving it for publication was Chuan Li. subsurface porosity, including but not limited to the analysis of lithological core samples and well logging [6], [7], which are often challenging, time-consuming, and expensive [8]. Seismic inversion-based techniques have been an alternative.…”

Section: Introductionmentioning

confidence: 99%

Ensemble Deep Learning-Based Porosity Inversion From Seismic Attributes

et al. 2023

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Underground porosity is important in many earth sciences and engineering fields, including hydrocarbon reservoir characterization and geothermal energy production. Popular methods largely rely on the analysis of lithological core data, well logs, and seismic inversion methods. While these methods are reliable, they are also time-consuming, expensive, and difficult to implement. In addition, seismic inversion has nonlinearity, data dimensionality, and non-uniqueness issues. However, deep learning (DL) can provide a more flexible, efficient, and accurate capability by mapping directly from seismic attributes to underground porosity. Therefore, we trained several DL models with different optimization functions. In the training steps, we labelled every seismic attribute data point with its corresponding porosity derived from the welllogs. In contrast to popular ensemble techniques, we proposed a weighted prediction approach based on the strengths of each model. Testing results showed a coefficient of determination (R 2 ) of 0.94345 and a Pearson's correlation coefficient of 0.9725 between the actual model and the model of the proposed approach, versus 0.9681 and 0.9716 for the best single and popular ensemble models, respectively. Further, we tested the effectiveness of our method using real seismic data from the North Sea. With a Pearson's correlation value of 0.9743, the inverted model ranges from 27 to 35%, compared to the reference model, which has an overall range of 20 to 33%. These results provide insights into the potential of the proposed method and its applicability to any other seismic volume to determine spatially varying underground porosity from seismic attributes directly.INDEX TERMS Deep neural networks, ensemble methods, seismic inversion, reservoir properties.

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Facies and Lithofacies Classifications from Well Logs

Cited by 6 publications

References 14 publications

Bayesian stochastic inversion with petro-elastic relation to quantify thin gas sands of Khadro Formation, Zamzama gas field

Bayesian stochastic inversion with petro-elastic relation to quantify thin gas sands of Khadro Formation, Zamzama gas field

Ensemble of Neural Networks Utilizing Seismic Attributes for Rock‐Property Inversion With Uncertainty Estimation

Ensemble Deep Learning-Based Porosity Inversion From Seismic Attributes

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