Abstract

Rock porosity is an important parameter for the formation evaluation, reservoir modeling, and petroleum reserve estimation. The conventional methods for determining the rock porosity are considered costly and time-consuming operations during the well drilling. This paper aims to predict the rock porosity in real time while drilling complex lithology using machine learning. In this paper, two intelligent models were developed utilizing the random forest (RF) and decision tree (DT) techniques. The drilling parameters include weight on bit, torque, standpipe pressure, drill string rotation speed, rate of penetration, and pump rate. Two datasets were employed for building the models (3767 data points) and for validating the developed models (1676 data points). Both collected datasets have complex lithology of carbonate, sandstone, and shale. Sensitivity and optimization on different parameters for each technique were conducted to ensure optimum prediction. The models' performance was checked by four performance indices which are coefficient of determination (R2), average absolute percentage error (AAPE), variance account for (VAF), and a20 index. The results indicated the strong porosity prediction capability for the two models. DT model showed R2 of 0.94 and 0.87 between the predicted and actual porosity values with AAPE of 6.07 and 9% for training and testing, respectively. Generally, RF provided a higher level of strong prediction than DT as RF achieved R2 of 0.99 and 0.90 with AAPE of 1.5 and 7% for training and testing, respectively. The models' validation proved a high prediction performance as DT achieved R2 of 0.88 and AAPE of 8.58%, while RF has R2 of 0.92 and AAPE of 6.5%.

Highlights

  • Research ArticleIntelligent Prediction for Rock Porosity While Drilling Complex Lithology in Real Time

  • Rock porosity is an important parameter for the formation evaluation, reservoir modeling, and petroleum reserve estimation. e conventional methods for determining the rock porosity are considered costly and time-consuming operations during the well drilling. is paper aims to predict the rock porosity in real time while drilling complex lithology using machine learning

  • Introduction e porosity of the rock is commonly defined as the ratio between the void pore spaces in the rock to the total bulk volume for the rock, and this space will provide the storage capacity for the petroleum fluids if it is connected. e rock porosity is a vital petrophysical property as it has a great impact on the reservoir reserve estimation, and as a result, for the field development decision-making [1, 2]. e precise determination of the rock porosity will significantly affect the petroleum reserve estimation and economics [3], and the accuracy of the porosity determination will play a huge role

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Summary

Research Article

Intelligent Prediction for Rock Porosity While Drilling Complex Lithology in Real Time. Rock porosity is an important parameter for the formation evaluation, reservoir modeling, and petroleum reserve estimation. E conventional methods for determining the rock porosity are considered costly and time-consuming operations during the well drilling. Is paper aims to predict the rock porosity in real time while drilling complex lithology using machine learning. Determining the rock porosity can be achieved practically by direct and indirect measurement or prediction using empirical equations. Each technique has its pros and cons from technical and economic aspects as the lab direct measurements for the porosity is considered the most relative accurate way; this technique is costly and time-consuming and covers only the cored interval within the reservoir or the drilled sections [6, 7]. A recent technique is introduced to the field applications of rock characterization and rock porosity measurement by employing the drilled cuttings; the technique required special cuttings size and advanced sample preparation [10]

Computational Intelligence and Neuroscience
Data points
Low accuracy results
Correlation coefficient
Leaf node
Optimum value
Porosity log Actual porosity Predicted porosity
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