This study offered a detailed review of data sciences and machine learning (ML) roles in different petroleum engineering and geosciences segments such as petroleum exploration, reservoir characterization, oil well drilling, production, and well stimulation, emphasizing the newly emerging field of unconventional reservoirs. The future of data science and ML in the oil and gas industry, highlighting what is required from ML for better prediction, is also discussed. This study also provides a comprehensive comparison of different ML techniques used in the oil and gas industry. With the arrival of powerful computers, advanced ML algorithms, and extensive data generation from different industry tools, we see a bright future in developing solutions to the complex problems in the oil and gas industry that were previously beyond the grip of analytical solutions or numerical simulation. ML tools can incorporate every detail in the log data and every information connected to the target data. Despite their limitations, they are not constrained by limiting assumptions of analytical solutions or by particular data and/or power processing requirements of numerical simulators. This detailed and comprehensive study can serve as an exclusive reference for ML applications in the industry. Based on the review conducted, it was found that ML techniques offer a great potential in solving problems in almost all areas of the oil and gas industry involving prediction, classification, and clustering. With the generation of huge data in everyday oil and gas industry activates, machine learning and big data handling techniques are becoming a necessity toward a more efficient industry.

/pdf/a-systematic-review-of-data-science-and-machine-learning-1p4idjzvsn.pdf

A systematic review of data science and machine learning applications to the oil and gas industry

Real-time optimization of drilling parameters based on mechanical specific energy for rotating drilling with positive displacement motor in the hard formation

Methods, computer programs, and systems for detecting at least one downhole condition are disclosed. Forces are measured at a plurality of locations along the drillstring. The drillstring includes a drillpipe. At least one of the forces is measured along the drillpipe. At least one downhole condition is detected based, at least in part, on at least one measured force.

Multiple distributed force measurements

Systems and method for downhole measurement and communications are disclosed and shown in Figure 3. The system includes a communications medium (310), at least partially disposed in a drillpipe (140), a processor coupled to the communications medium, at least two sensor modules (175) coupled to the communications medium, where at least one of the sensor modules is along a drillpipe, and at least one communications coupler to couple at least one sensor module to the communications medium.

Multiple distributed sensors along a drillstring

Development and Utilization of a Bit Based Data Acquisition System in Hard Rock PDC Applications

On-Line Torque & Drag: A Real-Time Drilling Performance Optimization Tool

The Benefits of Monitoring Torque & Drag in Real Time

Measurement of Mud Motor Rotation Rates using Drilling Dynamics

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On-Line Torque & Drag: A Real-Time Drilling Performance Optimization Tool

The Benefits of Monitoring Torque & Drag in Real Time

Measurement of Mud Motor Rotation Rates using Drilling Dynamics