An intelligent-while-drilling steering method of global closed-loop servo control [大闭环伺服控制随钻智能导向钻井方法]

作者：Wu S.; Li S.; Chen D.; Li X.; Du A.; Zhang Y.

来源：Acta Geophysica Sinica, 2021, 64(11): 4215-4227.

DOI：10.6038/cjg2021O0449

• Artificial intelligence
• Deep learning
• Geosteering
• Intelligent drilling
• Large closed-loop servo control
• Logging While Drilling (LWD)
• Machine learning

The reserves of deep oil and gas resources are huge, making them significant for global oil and gas development. As the drilling depth goes towards to deep (>4500 m) and ultra-deep (>6000 m) formations, the geological conditions becomes more complex, the transmission rate of drilling mud signal is limited. The delay in the downhole logging while drilling data transmission would increase the risks of drilling accidents and drilling out of the reservoir. The current drilling site decision-making method is not applicable, and the downhole autonomous intelligent drilling will be an important direction of deep and ultra-deep drilling operations. Referring to the theoretical and technical framework of autonomous car, a global closed-loop servo control intelligent drilling method is proposed. This method integrates rotary steering, geosteering, seismic while drilling, far-field electromagnetic measurement, measurement while drilling, signal transmission, automatic drill rig and other technologies. After using the "learning while drilling" approach, the artificial intelligence evaluation and decision method, it is able to intelligent identification of sweet spot in front of drill bit, intelligent determination of drilling direction and rate of penetration, and allow the drill bit automatically navigate and drill downhole with the global closed-loop servo control. The global closed-loop servo control intelligent drilling system framework includes three parts: drilling perception, intelligent decision-making and global closed-loop control. The drilling perception part obtains the bit position and the characteristic parameters of formations around and in front of the well through the logging while drilling data. Based on the information obtained by the drilling perception part, the intelligent decision-making part uses the AI (Artificial Intelligence) decision-making model to update the well path and optimize the drilling strategy. The global closed-loop control part adjusts the drilling direction and rate of penetration according to the intelligent decision instruction. In the drilling perception part, support vector machine learning algorithm is used to intelligently identify lithology using logging while drilling data. Random forest algorithm and LSTM (Long Short Term Memory) recurrent neural network are used to evaluate porosity, permeability, saturation and shale content. The intelligent decision-making part uses the random forest algorithm to predict and optimize the rate of penetration. They all have achieved high accuracy.

• 单位
  中国科学院大学; 油气资源与探测国家重点实验室; 中国石油大学（华东）