智能化破岩与工况识别方法数据集

数据集服务于“钻井过程闭环控制与智能决策”核心目标，数据集由两大部分构成：其一为智能破岩室内实验数据，内含机械钻速预测与优化、PDC齿破岩效率评价、脉冲射流提速机理及钻头破岩实验等子集，旨在揭示高效破岩机制；其二为井下工况识别系统测试数据，包括基于大数据的钻头优选、随钻地层岩性识别、工况智能诊断平台测试数据以及钻头泥包、振动、磨损等关键工况样本，为算法研发与系统验证提供支撑。本数据集融合了可控实验与珍贵现场数据，具有稀缺性和高质量特点，能为智能钻头设计、井下实时工况诊断、钻井参数优化等研究提供直接、可靠的测试样本，对推动智能钻井技术的创新与发展具有重要的基础性价值和广泛的应用前景。
本数据集中的实验数据基于大型室内岩石力学实验平台与现场随钻测量系统的多类传感器产生，综合记录了机械钻速、钻压、扭矩、转速、振动、声波、地层伽马值、井下压力、流量以及PDC齿破岩效率等关键观测值。现场数据采集来源于油气井场多个监测节点，融合了多类型传感设备，包括但不限于：（1）地面机械参数传感器，用于采集钻速、钻压、转速、扭矩、泵冲等实时录井参数；（2）流体相关参数监测仪器，用于获取立压、套压、进/出口钻井液排量等实时录井参数；（3）井下测量仪器，用于获取声波时差、自然伽马等表征地层性质的测井数据。数据量247MB。

This dataset serves the core goal of "closed-loop control and intelligent decision-making during drilling operations". It consists of two main parts:
The first part is indoor intelligent rock-breaking experimental data, which includes subsets such as rate of penetration (ROP) prediction and optimization, PDC cutter rock-breaking efficiency evaluation, pulse jet speed-up mechanism, and bit rock-breaking experiments, aiming to reveal the efficient rock-breaking mechanism.
The second part is downhole condition identification system test data, including big data-based bit selection, while-drilling formation lithology identification, test data of the intelligent condition diagnosis platform, and key condition samples such as bit balling, vibration, and wear, providing support for algorithm development and system verification.

This dataset integrates controlled laboratory experiments and precious field data, featuring scarcity and high quality. It can provide direct and reliable test samples for researches such as intelligent bit design, real-time downhole condition diagnosis, and drilling parameter optimization, and has important fundamental value and broad application prospects for promoting the innovation and development of intelligent drilling technologies.

The experimental data in this dataset is generated by multiple types of sensors from large-scale indoor rock mechanics experimental platforms and field while-drilling measurement systems, comprehensively recording key observed values including rate of penetration, weight on bit (WOB), torque, rotational speed, vibration, acoustic wave, formation gamma value, downhole pressure, flow rate, and PDC cutter rock-breaking efficiency.
The field data is collected from multiple monitoring nodes at oil and gas well sites, integrating various sensing devices, including but not limited to: (1) Surface mechanical parameter sensors, which collect real-time logging parameters such as drilling speed, weight on bit, rotational speed, torque, and pump strokes; (2) Fluid-related parameter monitoring instruments, used to acquire real-time logging parameters such as standpipe pressure, casing pressure, inlet/outlet drilling fluid displacement; (3) Downhole measuring instruments, used to obtain logging data reflecting formation properties such as acoustic transit time and natural gamma. The total data volume is 247 MB.

搜集汇总

背景与挑战

背景概述

该数据集聚焦于智能钻井领域，包含智能破岩室内实验数据和井下工况识别系统测试数据两大部分，涵盖机械钻速预测、PDC齿破岩效率评价、随钻地层岩性识别及工况智能诊断等关键内容。数据集融合了可控实验与珍贵现场数据，具有稀缺性和高质量特点，数据量为247.7MB，包括8个文件，格式为xlsx、pdf等。它为智能钻头设计、井下实时工况诊断和钻井参数优化提供了直接可靠的测试样本，对推动智能钻井技术创新具有基础性价值和广泛应用前景。