Siddharth Misra

Associate Professor Texas A&M University

Siddharth Misra, SPE, is an associate professor at Texas A&M University who has published two books and developed nine technologies related to machine learning and electromagnetic sensing for energy and earth resource exploration. In 2018, he received the US Department of Energy Early Career Award, and in 2020 he was honored with four international awards for his contributions to exploration geophysics and subsurface engineering. Misra holds a bachelor's of technology degree in electrical engineering from the Indian Institute of Technology, Bombay, and a PhD degree in petroleum and geodesists engineering from The University of Texas at Austin. He can be reached at misra@tamu.edu.

AI/machine learning

Guest Editorial: What Engineers Should Know About the Expanding Generative AI Toolkit

You’ve heard of generative artificial intelligence, and odds are you’ve used it. But do you know how it works?

September 1, 2024 • Siddharth Misra • Journal of Petroleum Technology
Data & Analytics

Controlling Crack Propagation Using Reinforcement Learning

This article presents the application of a reinforcement learning control framework based on the Deep Deterministic Policy Gradient. The crack propagation process is simulated in Abaqus, which is integrated with a reinforcement learning environment to control crack propagation in brittle material. The real-world deployment of the proposed control framework is also dis…

August 8, 2023 • Siddharth Misra • Data Science and Digital Engineering
Energy transition

Guest Editorial: Is CO2 Removal Technology Key to a Sustainable Future?

The innovations in the carbon removal space are gaining attention for their potential to curb global climate change, but they each face particular challenges in implementation.

June 1, 2023 • Siddharth Misra • Journal of Petroleum Technology
Production

Adaptive Production Forecasting Using a Long Short-Term Memory Network

This article presents a deep-learning approach, the long short-term memory network, for adaptive hydrocarbon production forecasting that takes historical operational and production information as input sequences to predict oil production as a function of operational plans.

May 12, 2023 • Siddharth Misra • Data Science and Digital Engineering
Reservoir characterization

Infrasound Imaging Paints Clearer Reservoir Picture

This article presents a new data-driven analysis to locate low-frequency seismic sources, referred to as near-infrasound or infrasound sources. Combining these infrasound signals with microseismicity signals allows for better characterization and monitoring of the stimulated reservoir volume.

March 21, 2023 • Siddharth Misra • Data Science and Digital Engineering
Energy transition

Plummeting 'Energy Return on Investment' of Oil and the Impact on Global Energy Landscape

Our shifting energy landscape requires a new way to measure the amount of energy that can be extracted from any given source against the energy required to produce and distribute it.

March 20, 2023 • Siddharth Misra • Journal of Petroleum Technology
AI/machine learning

AI Paves the Way for a Sustainable Energy Future

The use of artificial intelligence in the clean energy sector increases the availability and accessibility of clean energy, making it a more viable and cost-effective alternative to traditional energy sources.

February 2, 2023 • Siddharth Misra • Data Science and Digital Engineering
Data & Analytics

Finding Low-Dimensional Representations of the Subsurface Using Deep Learning

Large geological models are needed for modeling the subsurface processes in geothermal, carbon-storage, and hydrocarbon reservoirs. The size of these models contributes to the computational cost of history matching, engineering optimization, and forecasting. To reduce this cost, low-dimensional representations need to be extracted. Deep-learning tools, such as autoenc…

November 16, 2022 • Siddharth Misra • Data Science and Digital Engineering
Data & Analytics

Unsupervised Learning: The Power of the Underdog

Supervised learning has many commercial applications; however, such learning lacks the capability to generate new insights and knowledge. In contrast, unsupervised learning discovers the inherent structures in unlabeled data, thereby helping generate new insights and actionable knowledge from large volumes of data.

July 27, 2022 • Siddharth Misra • Data Science and Digital Engineering