A New Resistivity-Based Model for Improved Hydrocarbon Saturation Assessment in Clay-Rich Formations Using Quantitative Geometry of the Clay Network

Garcia, Artur Posenato; Jagadisan, Archana; Rostami, Ameneh; Heidari, Zoya

doi:10.30632/pjv59n3-2018a3

Cited by 5 publications

(3 citation statements)

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“…In hydrocarbon reservoir characterization, the correct estimation of the rock matrix's mineral fractions is crucial in quantifying hydrocarbons. In studies related to reservoirs, in addition to directly impacting the calculation of porosity (Freedman et al., 2015), the knowledge of mineral composition can significantly improve the water saturation calculation (Clavier et al., 1984; Garcia et al., 2017; Poupon & Leveaux, 1971; Waxman & Smits, 1968) and estimate the cation exchange capacity (Herron, 1986) and total organic carbon (Gonzalez et al., 2013). Additionally, reliable mineralogy estimates can aid in monitoring a well's hydrocarbon/water contact during its productive life (North, 1987; Ulloa et al., 2016; Westaway et al., 1983) and aid in acid fracturing operations (Jin et al., 2019).…”

Section: Introductionmentioning

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: Introductionmentioning

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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“…ey concluded that joint elasticelectrical properties can reveal subtle rock responses to pressure, which are not discernible when observing elastic or electrical properties alone. Finally, Garcia et al [17] introduced a new resistivity-based model that quantitatively takes into account actual clay network geometry and the distribution and type of clay minerals present. Nevertheless, most studies have not quantitatively evaluated the degree of rock damage by establishing a damage variable based on resistivity.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Damage Evolution of Sandstone under Uniaxial Loading and Unloading Conditions Based on Resistivity Characteristics

Wang

Zhao

et al. 2019

Advances in Civil Engineering

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In complex rock engineering, understanding the stress state and determining stability and damage evolution are necessary. To more accurately provide a theoretical basis for judging the stress state of bedrock in engineering, this study experimentally addressed the damage evolution of sandstone under loading and unloading conditions. A theoretical relationship between rock resistivity and porosity was obtained according to the Archie formula, which allowed the derivation of the sandstone damage variable expression. Then, sandstone rock samples were used for experimental evaluation, and the feasibility of the theoretically determined damage variable was verified. Finally, through theoretical and experimental comparison analysis, we developed a correlative damage model for sandstone under uniaxial loading and unloading. The results show that the damage variable varies linearly with strain. The proposed correlative equation describes this behavior accurately for loading and unloading conditions. Based on the results of this study, the correlative damage model of sandstone under cyclic loading and unloading conditions can be further improved to be a complete constitutive damage model.

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“…Many models to calculate Sw were proposed since Archie's equation (Archie, 1942), some empirical and other analytical. However, empirical methods have low representativeness (e.g., Archie's equation does not work well in shaly sands or complex carbonates) and analytical solutions are often too complex and rely on many input parameters (e.g., see equations proposed by Garcia et al, 2017a andGarcia et al, 2017b). With machine learning, one is capable of training a model to predict Sw using the result of core analysis, which is the most reliable source, and their respective response in well logs.…”

Section: Machine Learning Applied To Well Logsmentioning

confidence: 99%

Modelagem geoquímica e mineralógica dos reservatórios carbonáticos do pré-sal da bacia de Santos através de perfis de poços e inteligência artificial.

Oliveira¹

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A New Resistivity-Based Model for Improved Hydrocarbon Saturation Assessment in Clay-Rich Formations Using Quantitative Geometry of the Clay Network

Cited by 5 publications

References 0 publications

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

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

Analysis of Damage Evolution of Sandstone under Uniaxial Loading and Unloading Conditions Based on Resistivity Characteristics

Modelagem geoquímica e mineralógica dos reservatórios carbonáticos do pré-sal da bacia de Santos através de perfis de poços e inteligência artificial.

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