Search results for:

This paper contains a detailed discussion of methods and a software tool that has been developed to generate information that predicts formation-face pressures in real time with the help of live bottomhole-pressure data.

The fourth industrial revolution is taking the oil and gas business by storm. Many companies have increased resources for big-data analytics and machine learning. Though no one sees physical oilfield services as in decline, development in these areas may take a back seat to artificial intelligence.

In this paper, the authors study the stress status of CT string held by gripper blocks, the axial force distribution of gripper blocks, and the longitudinal resonance of CT strings in vertical wells.

This paper discusses the advantages of the in-line quench-and-temper (Q&T) process, which enhances overall CT life and reliability by producing tubing with more-uniform microstructure throughout its entire length, increased material strength, and improved bend-fatigue performance.

A study by a real-time monitoring company showed that many coiled-tubing strings are retired with a lot of life left in them. It suggested companies could lower costs by using pipe for a longer time and could benefit from multicompany studies showing how their decisions compare to the competition.

In this paper, the authors show the limitation of CEOS for modeling reservoir behavior of liquid-phase black and volatile oil in highly undersaturated reservoirs.

Trade tensions and economic sentiment take a toll.

In this paper, the authors present an open-source tool kit for the generation of microfabricated transparent models of porous media (micromodels) from image data sets using optically transparent 3D polymer additive manufacturing (3D printing or sintering).

In my view, we still do not possess a full understanding of oil production in unconventional fractured reservoirs. Our ability to forecast such assets remains elusive, even with copious amounts of analytics, mountains of data, and an arsenal of machine-learning tools.

Exponential thinking is called the “exponential surprise factor.” These underpinnings are observed on the tubular mechanics side also through data analytics, machine learning, artificial intelligence, and cognitive processes.