Spatial interpolation of coal properties using geographic quantile regression forest

Maxwell, Kane; Rajabi, Mojtaba; Esterle, Joan

doi:10.1016/j.coal.2021.103869

Cited by 9 publications

(3 citation statements)

References 30 publications

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“…Maxwell proposed a quantile regression forest algorithm as an alternative method to spatially model coal properties. 39 Other examples based on RF include predicting coal wettability for CO 2 sequestration purpose, 40 identifying altered and nonaltered lithotypes, 41 solar power forecast, 42 forecasting accuracy of daily enterprise electricity consumption, 43 and so forth. However, the aforementioned approaches primarily concentrate on data preprocessing, feature selection, hyperparameter optimizing, and model performance.…”

Section: Resultsmentioning

confidence: 99%

“…A comparative analysis suggested that the final output shows better than 90% classification accuracy compared to ground truth. Maxwell proposed a quantile regression forest algorithm as an alternative method to spatially model coal properties . Other examples based on RF include predicting coal wettability for CO 2 sequestration purpose, identifying altered and nonaltered lithotypes, solar power forecast, forecasting accuracy of daily enterprise electricity consumption, and so forth.…”

Section: Resultsmentioning

confidence: 99%

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Analysis of Factors of Productivity of Tight Conglomerate Reservoirs Based on Random Forest Algorithm

Wang

Jiang

et al. 2022

ACS Omega

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The tight conglomerate reservoir of Baikouquan formation in the MA 131 well block in the Junggar basin abounds with petroleum reserves, yet the vertical wells in this reservoir have achieved a limited development effect. The tight conglomerate reservoirs have become an important target for exploration and exploitation. The high-efficiency development scheme of a small well spacing three-dimensional (3D) staggered well pattern has been determined by a series of field tests on well pattern and well spacing development. Multistage fracturing with a horizontal well has been demonstrated as the primary development technology. The horizontal wells in the MA 131 small well spacing demonstration area have achieved significantly different development effects, and the major controlling factors for high and stable production of a single well remain unclear. In this study, we proposed an evaluation model of major productivity controlling factors of the tight conglomerate reservoir to provide a reference for oil recovery based on a random forest (RF) machine-learning algorithm. The productivity factors were investigated from two aspects: petrophysical facies that are capable of indicating the genetic mechanism of geological dessert and engineering dessert parameters forming complex fracture networks. Resultantly, the reservoir in the MA 131 well block can be classified into 12 petrophysical facies according to the sedimentary characteristics and diagenesis analysis. The mercury injection curves of a variety of petrophysical facies can be classified into four reservoir quality types. The RF model was trained on 80% of the data to predict the oil well class using the selected features as primary inputs while the remaining 20% of the data were set to test the model performance. The results indicated that the RF model produced excellent results with only 12 misclassifications across the entire data set of 627 samples that represent <2% error. The important evaluation score of the random forest algorithm model showed that the reservoir type, oil saturation, horizontal stress difference, and gravel content are the most important four indicators, with each value exceeding 15%. Brittleness and maximum horizontal stress are considered the least important indexes, with values of less than 5%. Reservoir quality and oil saturation were confirmed as the major controlling factors and material foundation for oil wells’ high and stable production. As indicated in this study, stress difference and gravel content are the major controlling factors in the formation of a complex fracture network.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Analysis of Factors of Productivity of Tight Conglomerate Reservoirs Based on Random Forest Algorithm

Wang

Jiang

et al. 2022

ACS Omega

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“…They found the methodology flexible in such a way that it could be extended to other ecological phenomena that are dependent on meteorological features. Maxwell et al (2021) also addressed weaknesses in geostatistical methods to model coal properties by proposing a QRF algorithm. The algorithm performed better than the most popular regression kriging method in the field of geostatistics.…”

Section: Related Literaturementioning

confidence: 99%

An evaluation of variable selection methods using Southern Africa solar irradiation data

Maposa,

Masache,

Mdlongwa

et al. 2024

J. energy South. Afr.

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Dimensionality poses a challenge in developing quality predictive models. Often when modelling solar irradiance (SI), many covariates are considered. Training such data has several disadvantages. This study sought to identify the best variable embedded selection method for different location and time horizon combinations from Southern Africa solar irradiance data. It introduced new variable selection methods into solar irradiation studies, namely penalised quantile regression (PQR), regularised random forests (RRF), and quantile regression forest (QRF). Stability analysis, performance and accuracy metric evaluations were used to compare them with the common lasso, elastic and ridge regression methods. The QRF model performed best in all locations followed by the shrinkage methods on hourly data. However, it was found that QRF is not sensitive to associations through correlations, thereby ignoring the relevance of variables while focusing on importance. Among the shrinkage methods, the lasso performed best in only one location. On the 24-hour horizon, elastic net dominated the performances among the shrinkage methods, but QRF was best in three locations of the six considered. Results confirmed that variable selection methods performed differently on different situational data sets. Depending on the strengths of the methods, results were combined to identify the most paramount variables. Day, total rainfall, and wind direction were superfluous features in all situations. The study concluded that shrinkage methods are best in cases of extreme multicollinearity, while QRF is best on data sets with outliers or/and heavy tails.

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Impact of In-Situ Density Spatial Model Methods on Resource Tonnages in Highly Intruded Coal Deposits

2022

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Spatial interpolation of coal properties using geographic quantile regression forest

Cited by 9 publications

References 30 publications

Analysis of Factors of Productivity of Tight Conglomerate Reservoirs Based on Random Forest Algorithm

Analysis of Factors of Productivity of Tight Conglomerate Reservoirs Based on Random Forest Algorithm

An evaluation of variable selection methods using Southern Africa solar irradiation data

Impact of In-Situ Density Spatial Model Methods on Resource Tonnages in Highly Intruded Coal Deposits

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