钻井自动化

机器学习算法优化海上钻井中心位置

本文提出了一种使用基于与井成本相关的多个参数的算法来优化井口塔位置的方法。

使用开发的算法生成的井轨迹示例。
图 1——使用所开发的算法生成的良好轨迹示例。
来源:SPE 211772

长期发展规划需要加密钻探。由于海上环境中的槽位数量有限,因此需要新的钻井中心(井口塔)。钻探中心的优化选址对于降低钻探费用可发挥重要作用。完整的论文讨论了与井口塔放置相关的两个主要挑战:首先,根据地下坐标和钻井约束将未来的钻井中心放置在哪里;其次,如何将数十个地下目标分配给多个钻井中心。

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Drilling automation

Machine-Learning Algorithms Optimize Drilling-Center Locations Offshore

This paper presents an approach to optimize the location of wellhead towers using an algorithm based on multiple parameters related to well cost.

Well-trajectory examples generated with the developed algorithms.
Fig. 1—Well-trajectory examples generated with the developed algorithms.
Source: SPE 211772

Long-term development planning requires infill drilling. Because of the limited number of slots in the offshore environment, new drilling centers (wellhead towers) are required. Optimized location of drilling centers can play a major role in reducing drilling expenses. Two main challenges related to wellhead-tower placement are discussed in the complete paper: first, where to place future drilling centers based on subsurface coordinates and drilling constraints; and second, how to allocate dozens of subsurface targets to multiple drilling centers.

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