WITHDRAWN: Different methods for lithology and mineralogy recognition

Khamees, Luay Ahmed; Alrazzaq, Ayad A. Alhaleem A.

doi:10.1016/j.matpr.2021.04.531

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…Some of the main traditional lithological interpretation methods are the neutron-density cross plot and the M-N lithology cross plot. Consequently, the traditional lithological interpretation of logs is applied to construct the performance control parameter curve, which is used to monitor the machine learning methods' performance and to supply the unavailable hard lithological data (Khamees et al, 2021). Based on these two traditional…”

Section: Lithology Interpretationmentioning

confidence: 99%

Comparative Analysis of the K-Nearest-Neighbour Method and K-Means Cluster Analysis for Lithological Interpretation of Well Logs of the Shushufindi Oilfield, Ecuador

Gómez¹,

Flores²,

Vadászi³

2022

MGPB

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The lithological interpretation of well logs is a fundamental task in Earth science that can be accomplished with the application of various machine learning algorithms. The current investigation attempts to evaluate the performance of the K-nearest-neighbour Density Estimate (KNN) and K-means cluster analysis methods for predicting lithology in a dataset of logs measured in the siliciclastic reservoir of the Shushufindi Oilfield of Ecuador. The comparison of lithological interpretation is assembled using classical methods, such as qualitative interpretation and density-neutron cross plot. The lithological interpretation results showed that the supervised method KNN has a higher fitting level with the comparison interpretation data (87.3%, 1145 m predicted of 1311.1 m interpreted) than the results of the K-means method (71.6%, 939.7 m predicted of 1311.1 m interpreted). The geological nature of the reservoir creates a level of a discrepancy because of the near geophysical responses between limestone and intermedia grain size rocks. The possibility of controlling this in the KNN algorithm makes it preferable for usage in these types of reservoir lithological interpretation.

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Section: Lithology Interpretationmentioning

confidence: 99%

Comparative Analysis of the K-Nearest-Neighbour Method and K-Means Cluster Analysis for Lithological Interpretation of Well Logs of the Shushufindi Oilfield, Ecuador

Gómez¹,

Flores²,

Vadászi³

2022

MGPB

View full text Add to dashboard Cite

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Lithology Classification and Porosity Estimation of Tight Gas Reservoirs With Well Logs Based on an Equivalent Multi-Component Model

et al. 2022

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Tight gas makes up a significant portion of the natural gas resources. There are tight gas reservoirs with great reserve and economic potential in the west Sichuan Basin, China. Due to the complex mineral component and heterogeneity of the thick tight sand formations, the reservoir parameters are challenging to evaluate from well logs using conventional methods, even the fundamental porosity. The mineral components must be considered. In this study, based on the analysis of different logging responses of varying lithologies, we introduced the complex reservoir analysis (CRA) method. CRA is always used in the carbonate reservoirs to calculate the different rock component volume fractions and can be used to classify the lithology and calculate the porosity simultaneously. By analyzing the component, a new equivalent component method (CRAE) is proposed based on the CRA method in this paper. In this method, the AC-CNL equation-calculated porosity is calibrated according to the core porosity data to set the rock components’ physical parameters. After calibration, the rock component fractions and porosity can be calculated accurately. Also, according to the relationship between the grain size and natural gamma-ray, a granularity median model was established. Six lithology types, including coarse-grained quartz sandstone and coarse-grained lithic sandstone, are distinguished, and the porosity is estimated in the study area. The identification results are compared with the mud logging data and other methods. It shows that this method is very well adequate in the tight sandstone gas reservoirs in the study area.

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WITHDRAWN: Different methods for lithology and mineralogy recognition

Cited by 2 publications

References 9 publications

Comparative Analysis of the K-Nearest-Neighbour Method and K-Means Cluster Analysis for Lithological Interpretation of Well Logs of the Shushufindi Oilfield, Ecuador

Comparative Analysis of the K-Nearest-Neighbour Method and K-Means Cluster Analysis for Lithological Interpretation of Well Logs of the Shushufindi Oilfield, Ecuador

Lithology Classification and Porosity Estimation of Tight Gas Reservoirs With Well Logs Based on an Equivalent Multi-Component Model

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