Abstract

The common scientific issue presented by lithology prediction of tight sandstone reservoirs is that most primary lithologies cannot be distinguished effectively within the classic crossplots since they universally have similar logging responses. Lithology prediction actually is an issue of pattern recognition, and has been proved its best solver currently is machine learning technique. XGBoost is demonstrated to be a high-efficient predictor, while two factors, setting of hyper-parameters and dimensionality reduction of learning dataset, severely limit its computational performance. Genetic algorithm-particle swarm optimization (GA-PSO) and continuous restricted Boltzmann machine (CRBM) are introduced to optimize hyper-parameters and reduce amount of learning variables for the calculation of XGBoost, respectively, then a smart predictor CRBM-GA-PSO-CRBM proposed. Data used for validation is collected from the wells in the member of Chang 4 + 5, Jiyuan Oilfield, Ordos Basin. Three experiments are designed to verify prediction capability of the proposed model. Compared to other validated models, the higher prediction accuracies are all generated by the proposed model in three experiments, well expounding the proposed model is capable to produce reliable predicted lithologies, and has a better generalization and application prospect in the study field of lithology prediction.

摘要

致密砂岩储层的岩性预测所提出的普遍科学问题是，在经典的交叉图中，由于它们普遍具有相似的测井响应，因此无法有效地区分大多数原始岩性。岩性预测实际上是模式识别的一个问题，目前已证明其最佳求解器是机器学习技术。XGBoost被证明是一种高效的预测器，而超参数的设置和学习数据集的降维这两个因素严重限制了其计算性能。引入了遗传算法-粒子群算法（GA-PSO）和连续受限玻尔兹曼机（CRBM）来分别优化超参数并减少用于XGBoost计算的学习变量的数量，然后提出了智能预测器CRBM-GA-PSO-CRBM。用于验证的数据是从鄂尔多斯盆地济源油田长4 + 5段的井中收集的。设计了三个实验以验证所提出模型的预测能力。与其他经过验证的模型相比，所提出的模型在三个实验中均产生了更高的预测精度，很好地阐述了所提出的模型能够产生可靠的预测岩性，在岩性预测研究领域具有更好的推广和应用前景。