基于深度学习和基于种群的全局优化器的成熟油田间歇产气数据驱动优化

J. F. Gómez, P. S. Omrani, S. Belfroid
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摘要

在气井中,由于难以预测的后期现象(如液体加载和驱油)造成的动态影响,可能会导致产量下降或不稳定。为了最大限度地减少这些影响的负面影响,最大限度地提高产量并延长油井的使用寿命,油井通常采用间歇生产模式。这项工作的目标是找到最佳的生产和关井周期,以最大限度地提高间歇产气量,为作业者提供决策支持。通过将基于历史数据训练的深度学习前向模型与基于种群的全局优化器粒子群优化(PSO)相结合,开发了适用于单井和多井的框架。正演模型分别预测生产和关井期间的产量和井口压力。PSO算法在给定的操作和环境目标下优化操作标准,例如最大化产量、最小化启动/关闭操作、在计划维护和满足合同生产价值等约束条件下惩罚排放。深度学习模型的准确性在综合数据和现场数据上进行了测试。在合成数据的基础上,对成熟井进行了不同储层条件下的测试,如初始含水饱和度、渗透率和流动形式。综合数据预测总累积产量的相对误差为0.5 ~ 4.6%,现场数据预测的相对误差为0.9%。压力预测的平均误差为2-3 bar。优化框架对单井(现场数据)和井群(综合数据)的生产优化和合同价值匹配进行了基准测试。北海一口井的单井产量优化实现了3%的增产,包括计划的维护。对6口井进行了生产优化,在30天的时间内,产量增加了21%,而合同价值匹配的产量在目标的3%内达到29/30。以临界压力/气体流量为操作标准,获得了最优、可重复且计算效率最高的结果。这可以在各种井况和作业要求下实现实时产气优化和作业决策。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Data-Driven Optimization of Intermittent Gas Production in Mature Fields Assisted by Deep Learning and a Population-Based Global Optimizer
In gas wells, decreased/unstable production can occur due to difficult-to-predict dynamic effects resulted from late-life phenomena, such as liquid loading and flooding. To minimize the negative impact of these effects, maximize production and extend the wells’ lifetime, wells are often operated in an intermittent production regime. The goal of this work is to find the optimum production and shut-in cycles to maximize intermittent gas production as a decision support to operators. A framework suitable for single and multiple wells was developed by coupling a Deep Learning forward model trained on historical data with a population-based global optimizer, Particle Swarm Optimization (PSO). The forward model predicts the production rates and wellhead pressure during production and shut-in conditions, respectively. The PSO algorithm optimizes the operational criteria given operational and environmental objectives, such as maximizing production, minimizing start-up/shut-in actions, penalizing emissions under several constraints such as planned maintenances and meeting a contract production value. The accuracy of the Deep Learning models was tested on synthetic and field data. On synthetic data, mature wells were tested under different reservoir conditions such as initial water saturation, permeability and flow regimes. The relative errors in the predicted total cumulative production ranged between 0.5 and 4.6% for synthetic data and 0.9% for field data. The mean errors for pressure prediction were of 2-3 bar. The optimization framework was benchmarked for production optimization and contract value matching for a single-well (on field data) and a cluster of wells (synthetic data). Single-well production optimization of a North Sea well achieved a 3% production increase, including planned maintenances. Production optimization for six wells resulted in a 21% production increase for a horizon of 30 days, while contract value matching yielded 29/30 values within 3% of the target. The most optimum, repeatable and computationally efficient results were obtained using critical pressure/gas flowrates as operational criteria. This could enable real-time gas production optimization and operational decision-making in a wide range of well conditions and operational requirements.
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