渤海区域基于数据驱动的钻井提速

doi:10.11885/j.issn.1674-5086.2020.06.09.02

摘要/Abstract

摘要： 随着钻井作业深度的增加，地层条件和井身结构变得复杂，钻井投入增加。为了提高钻井效率，降低钻井成本，在钻井过程中，从录井数据出发，结合神经网络和遗传算法，找出了适用于渤海某区域不同地层的最优机械钻速及其对应的钻井参数（钻压，转速和排量），从而保证了高效钻井作业。收集渤海地区某区块不同井的明化镇和馆陶组两个地层段8 000组数据（每层4 000组），针对每一地层单独训练机器学习模型。以其中一层为例，首先将3 900组钻井参数作为输入，对应的机械钻速作为输出训练BP神经网络；然后将剩余的100组钻井参数作为输入数据，利用得到的神经网络对此时的机械钻速进行预测；最后将4 000组钻井参数作为遗传算法中的种群个体，将预测的机械钻速作为遗传算法中的一个重要参数个体适应度值，并通过遗传算法推导最优机械钻速及其对应的钻井参数。提出的方法充分利用了油田现场的数据，得到了适用于渤海地区不同地层段的机器学习模型，提高了机械钻速，实现了钻井提速。

关键词: 钻井提速, 钻井参数优化, 神经网络, 遗传算法, 数据驱动

Abstract: With the increase of drilling operating depth, formation conditions and well structure become complicated, resulting in increasement in drilling investment. In order to improve the drilling efficiency and reduce the drilling cost, this paper used the logging data obtained in the drilling process, combined with the neural network and GA, and finds the optimal ROP and its corresponding drilling parameters, and ensured the efficient drilling operations. Collect 8 000 sets of data for 2 stratigraphic sections of different wells in a block in the Bohai Area (4 000 sets per layer), and train the machine learning model separately for each stratum. Taking one of the layers as an example, 3 900 sets of drilling parameters are taken as inputs, and the corresponding 3 900 sets of ROP are used as output to train the BP neural network; then the remaining 100 sets of drilling parameters are taken as input data. The obtained neural network is used to predict 100 sets of ROP. Finally, 4 000 sets of drilling parameters are used as the population individuals in the genetic algorithm, and the predicted ROP is used as one important parameter in the GA-the individual fitness value, the optimal ROP and its corresponding drilling parameters are derived by GA. The method makes full use of the data of the oilfield site, and obtains machine learning models suitable for different strata in the Bohai Area, which improves the ROP and realizes the drilling speed.

Key words: drilling speed, drilling parameter optimization, neural network, genetic algorithm, data-driven

中图分类号:

TE22

刘兆年, 赵颖, 孙挺. 渤海区域基于数据驱动的钻井提速[J]. 西南石油大学学报(自然科学版), 2020, 42(6): 35-41.

LIU Zhaonian, ZHAO Ying, SUN Ting. Data-driven Drilling Acceleration in Bohai XX Block[J]. Journal of Southwest Petroleum University(Science & Technology Edition), 2020, 42(6): 35-41.

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