|Theme||Visible||Selectable||Appearance||Zoom Range (now: 0)|
A ROP optimization methodology is presented, using offset drilled wells data. Data is used to train a neural network model for different rock properties which is presented in the vertical section of the reservoir. By changing the drilling parameters like Weight on Bit, Rate of Penetration (ROP) for the target well is optimized. Offset drilled wells data is used to train neural network; the inputs to the model are RPM,WOB (Weight on Bit) and Mud Flow Rate as well as ROP of the offset wells; the output is the new setpoints for input parameters to get the best ROP on the target well. Using offset well drilling data, rock properties of different formations drilled will be calculated explicitly.
Measuring the flow of water, mud, and cuttings from a well is critical, and difficult. A new flowmeter design that promises to be both accurate and durable is one of three technologies featured in a JPT series on drilling measurement innovation. This paper describes a virtual metering tool that can monitor well performance and estimate production rates using real-time data and analytical models, integrating commercial software with an optimization algorithm that combines production and reservoir information. In this study, the authors investigated a fully data-driven approach using artificial neural networks (ANNs) for real-time virtual flowmetering and back-allocation in production wells. Australian technology developer MezurX is touting its newly introduced flow, density, and mud monitoring system as a significantly better alternative to the widely used Coriolis meter.
ConocoPhillips has deployed a low-code platform called Mendix to cut costs and increase efficiency, making it the first major oil and gas producer to use such technology across the company. A recent test proved the feasibility of using LiDAR on remote-controlled drones to create 3D maps of the inside of tanks, increasing the safety and efficiency of inspections. Three-dimensional seismic technology helped unlock more subsurface secrets for oil and gas operators. Now, 3D technology can be used in scanning, a cutting-edge technology that engineers can use to plan upgrades to oil and gas assets virtually. Despite streams of data being available on platforms about the condition of topside and drilling equipment, most experts agree that only a small fraction of such data is used.
Like biological brains, artificial neural networks may depend on slow-wave sleep for learning. The aerospace giant is launching a new business unit to cash in on the autonomous aviation market. It will act as a systems integrator for all Honeywell products and services that could be used in this industry. Artificial intelligence just seems to get smarter and smarter. But some of the improvement comes from tweaks rather than the core innovations their inventors claim—and some of the gains may not exist at all, says Davis Blalock, a computer science graduate student at MIT. Artificial intelligence systems can be trained to recognize visual content in drawings and provide a simplified context.
The course introduction will attempt to answer the question: How will A.I. change the way we work in the Oil and Gas industry in the coming years? Looking at what is underway in other industries and guessing what type of projects are under development in R&D departments in our industry will help answer that question. Oil and Gas examples will be presented corresponding to each of the terms A.I., Machine Learning, and Deep Learning, allowing participants to reach a clear understanding on how they differ. The course will then focus on Deep Learning (DL) and address all key aspects of developing and applying the technology to Oil and Gas projects. What is DL and how different is it from traditional neural networks?
Professionals and companies alike are increasingly turning to powerful techniques from the Artificial Intelligence (AI) domain, a move spurred on by the recent leaps in computational power, better algorithms, larger amounts of data being collected, the availability of more advanced software libraries, and the adoption of easy to use programming languages. This short course explores the basic concepts and techniques used in Machine Learning and Neural Networks, some of the technology's applications, and the need for data quality control.
A real-time deep-learning model is proposed to classify the volume of cuttings from a shale shaker on an offshore drilling rig by analyzing the real-time monitoring video stream. The best way to know how drilling affects drill bits is to visualize the bits. A device that creates high-resolution images for precise measurements is one of three technologies being featured in a JPT series on drilling measurement innovations. For the upstream industry, where improvement in efficiency or production can drive significant financial results, there is no question that the size of the digital prize is huge. A group of people who really care how drillers code the memos added to the daily drilling report is the data scientists—who find that the coded tags do not match the activity.
Artificial brains may need deep sleep in order to keep stable, a new study finds, much as real brains do. In the artificial neural networks now used for everything from identifying pedestrians crossing streets to diagnosing cancers, components dubbed neurons are supplied data and cooperate to solve a problem, such as recognizing images. The neural network repeatedly adjusts the interactions between its neurons and sees if these new patterns of behavior are better at solving the problem. Over time, the network discovers which patterns seem best at computing solutions. It then adopts these as defaults, mimicking the process of learning in the human brain.