Yu Zhichao, Wang Zhizhang, Jiang Qingping, Wang Jie, Zheng Jingrong, Zhang Tianyou
College of Geosciences, China University of Petroleum, Beijing 102249, China.
Xinjiang Oilfield Company Research Institute of Exploration & Exploitation, Karamay, Xinjiang 834000, China.
ACS Omega. 2022 Jun 3;7(23):20390-20404. doi: 10.1021/acsomega.2c02546. eCollection 2022 Jun 14.
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.
准噶尔盆地玛131井区百口泉组致密砾岩油藏石油储量丰富，但该油藏直井开发效果有限。致密砾岩油藏已成为勘探开发的重要目标。通过一系列井网与井距开发的现场试验，确定了小井距三维交错井网的高效开发方案。水平井多级压裂已被证明是主要的开发技术。玛131小井距示范区的水平井开发效果差异显著，单井高产稳产的主控因素尚不明确。在本研究中，我们基于随机森林（RF）机器学习算法，提出了致密砾岩油藏主要产能控制因素评价模型，为提高采收率提供参考。从两个方面研究了产能因素：能够指示地质甜点成因机制的岩石物理相和形成复杂裂缝网络的工程甜点参数。结果表明，根据沉积特征和成岩作用分析，玛131井区油藏可分为12种岩石物理相。各种岩石物理相的压汞曲线可分为四种储层质量类型。利用80%的数据训练RF模型，以所选特征作为主要输入来预测油井类别，同时将其余20%的数据用于测试模型性能。结果表明，RF模型在代表误差小于2%的627个样本的整个数据集中仅出现12次错误分类，效果极佳。随机森林算法模型的重要评价得分表明，油藏类型、含油饱和度、水平应力差和砾石含量是最重要的四个指标，每个指标的值均超过15%。脆性和最大水平应力被认为是最不重要的指标，值小于5%。储层质量和含油饱和度被确认为油井高产稳产的主要控制因素和物质基础。本研究表明，应力差和砾石含量是形成复杂裂缝网络的主要控制因素。
