State-of-the-art Openhole Shale Gas Logging

Franquet, Javier; Bratovich, Matt; Glass, Richard D.

doi:10.2118/160862-ms

Cited by 8 publications

(1 citation statement)

References 19 publications

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“…As the number of chemical elements provided by the tool may be insufficient to estimate the fractions of a vast and complex mineral assemblage, subsequent direct models proposed the inclusion of geochemical and geological inputs to deal with non‐singularity. These inputs can be constraints related to mass balance and mineral stoichiometry (Franquet et al., 2012) or the expected rock type (Quirein et al., 2010).…”

Section: Conceptual Backgroundmentioning

confidence: 99%

Hybrid mineral model integrating probabilistic and machine learning approaches for the Brazilian pre‐salt carbonate reservoirs

Oliveira

Freitas

Pesce

et al. 2023

Geophysical Prospecting

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Well mineralogy can be estimated from probabilistic, direct and machine learning models; however, all these models have limitations. The maximum number of components in probabilistic models is restricted to the number of logs plus one. Direct models require the precise composition of minerals. Machine learning models demand unbiased databases, a challenge as the samples are collected in reservoir intervals. These limitations impact the evaluation for the Santos Basin pre‐salt rocks due to the complexity of facies and magnesian clays. This work proposes creating a hybrid model through the combination of probabilistic and machine learning models. First, mineral fractions of calcite, dolomite, quartz, k‐feldspar, detrital clay, plagioclase and pyroxene are estimated by the algorithm XGBoost trained using rock samples. Then, a probabilistic model reconstructs the well logs and machine learning estimations through the seven minerals mentioned plus magnesian clays, pyrite, barite and fluids. The difference between the real and reconstructed responses is minimized, weighted by the curves’ uncertainties. The hybrid model is used to estimate the mineralogy of three wells drilled in the Santos Basin, honouring the mineralogy of the rock samples collected in these wells and improving the quantification of dolomite, pyroxene and magnesian clay. Among the advances introduced, the following stand out: The use of machine learning estimates and well logs improved the quantification of magnesian clay; the machine learning estimates regularized the probabilistic model, generating more coherent results; the uncertainties of the machine learning algorithms dealt with database bias. The hybrid model mitigated limitations related to database bias without the costs associated with collecting more samples.

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Section: Conceptual Backgroundmentioning

confidence: 99%