Artificial Intelligence Method for Shear Wave Travel Time Prediction considering Reservoir Geological Continuity

Abstract

The existing artificial intelligence model uses single-point logging data as the eigenvalue to predict shear wave travel times (DTS), which does not consider the longitudinal continuity of logging data along the reservoir and lacks the multiwell data processing method. Low prediction accuracy of shear wave travel time affects the accuracy of elastic parameters and results in inaccurate sand production prediction. This paper establishes the shear wave prediction model based on the standardization, normalization, and depth correction of conventional logging data with five artificial intelligence methods (linear regression, random forest, support vector regression, XGBoost, and ANN). The adjacent data points in depth are used as machine learning eigenvalues to improve the practicability of interwell and the accuracy of single-well prediction. The results show that the model built with XGBoost using five points outperforms other models in predicting. The R2 of 0.994 and 0.964 are obtained for the training set and testing set, respectively. Every model considering reservoir vertical geological continuity predicts test set DTS with higher accuracy than single-point prediction. The developed model provides a tool to determine geomechanical parameters and give a preliminary suggestion on the possibility of sand production where shear wave travel times are not available. The implementation of the model provides an economic and reliable alternative for the oil and gas industry.

Supervised machine learning for predicting shear sonic log (DTS) and volumes of petrophysical and elastic attributes, Kadanwari Gas Field, Pakistan

“…On other hand, ML appeared to be a successful tool capable of constructing a relationship between log curves based on their effective features for DTS prediction to evaluate the reservoir properties. Many researchers have recently used ML for predicting the DTS curve, that is, Bukar et al (2019), Anemangely et al (2019), Miah (2021), Gamal et al (2022), Gupta et al (2019), andLiu et al (2021). Due to complex reservoir attributes and limited data set, ML is a critical and optimized tool in the most productive LIB for predicting the DTS curve.…”

“…ML has emerged as a new way to approach technical problems, that is, unrecorded logs of wellbore or deficiencies in measured logs, by analyzing and generating reliable logs (Liu et al, 2021). SML is the basic technique of ML, and its algorithms create a model to link the data (or feature) vector to a matching label or target vector using training data when both the input and the related label are known and provided to the algorithm (Litjens, 2017).…”

“…SML is the basic technique of ML, and its algorithms create a model to link the data (or feature) vector to a matching label or target vector using training data when both the input and the related label are known and provided to the algorithm (Litjens, 2017). Log curve prediction or correction is the part of the regression model used to forecast the continuous numerical variables (Liu et al, 2021).…”

“…The λρ and μρ relationship is employed for better fluid and lithology delineation, which also highlights the gas sand zone and is strongly dependent on precise DTS (Young and Tatham, 2007). In many situations, DTS is not calibrated or has poor quality due to poor logging, failure of logging instruments, and high cost (Liu et al, 2021). Previously, rock physics modeling was used to compensate for these deficiencies by generating a set of Frontiers in Earth Science frontiersin.org consistent elastic logs.…”

Support Vector Regression Based on the Particle Swarm Optimization Algorithm for Tight Oil Recovery Prediction

“…With the increasing popularity of artificial intelligence in various fields around the world, such as applications in natural language processing technology, intelligent computing chips, unmanned system driving, and other core technologies, the technological expansion is also becoming more mature. − While the oil and gas industry acts as the lifeline of national energy development, machine learning also gradually expands deeply into the exploitation of oil and gas resources. − For example, Ma et al attempted to correlate stochastic reservoir parameters with observable features in production time series data using artificial intelligence techniques . These techniques can be integrated into the modeling process as an aid to predicting recoverable reserves.…”