Tariq Zeeshan, Mahmoud Mohamed, Abouelresh Mohamed, Abdulraheem Abdulazeez
King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia.
ACS Omega. 2020 Sep 30;5(40):26169-26181. doi: 10.1021/acsomega.0c03751. eCollection 2020 Oct 13.
Prediction of thermal maturity index parameters in organic shales plays a critical role in defining the hydrocarbon prospect and proper economic evaluation of the field. Hydrocarbon potential in shales is evaluated using the percentage of organic indices such as total organic carbon (TOC), thermal maturity temperature, source potentials, and hydrogen and oxygen indices. Direct measurement of these parameters in the laboratory is the most accurate way to obtain a representative value, but, at the same time, it is very expensive. In the absence of such facilities, other approaches such as analytical solutions and empirical correlations are used to estimate the organic indices in shale. The objective of this study is to develop data-driven machine learning-based models to predict continuous profiles of geochemical logs of organic shale formation. The machine learning models are trained using the petrophysical wireline logs as input and the corresponding laboratory-measured core data as a target for Barnett shale formations. More than 400 log data and the corresponding core data were collected for this purpose. The petrophysical wireline logs are γ-ray, bulk density, neutron porosity, sonic transient time, spontaneous potential, and shallow resistivity logs. The corresponding core data includes the experimental results from the Rock-Eval pyrolysis and Leco TOC measurements. A backpropagation artificial neural network coupled with a particle swarm optimization algorithm was used in this work. In addition to the development of optimized PSO-ANN models, explicit empirical correlations are also extracted from the fine-tuned weights and biases of the optimized models. The proposed models work with a higher accuracy within the range of the data set on which the models are trained. The proposed models can give real-time quantification of the organic matter maturity that can be linked with the real-time drilling operations and help identify the hotspots of mature organic matter in the drilled section.
预测有机页岩中的热成熟度指标参数对于确定油气前景和对油田进行合理的经济评估起着关键作用。页岩中的油气潜力是通过有机指标的百分比来评估的，如总有机碳（TOC）、热成熟温度、源岩潜力以及氢和氧指标。在实验室中直接测量这些参数是获得代表性值的最准确方法，但同时成本非常高。在缺乏此类设施的情况下，会采用其他方法，如解析解和经验关联式来估算页岩中的有机指标。本研究的目的是开发基于数据驱动的机器学习模型，以预测有机页岩地层地球化学测井的连续剖面。使用岩石物理电缆测井数据作为输入，以相应的实验室测量岩心数据作为巴尼特页岩地层的目标，对机器学习模型进行训练。为此收集了400多个测井数据和相应的岩心数据。岩石物理电缆测井包括伽马射线、体积密度、中子孔隙度、声波时差、自然电位和浅电阻率测井。相应的岩心数据包括岩石热解分析和Leco TOC测量的实验结果。本工作中使用了结合粒子群优化算法的反向传播人工神经网络。除了开发优化的PSO-ANN模型外，还从优化模型的微调权重和偏差中提取了明确的经验关联式。所提出的模型在其训练数据集范围内具有更高的精度。所提出的模型可以对有机质成熟度进行实时量化，这可以与实时钻井作业相关联，并有助于识别钻井剖面中成熟有机质的热点区域。
