DSDE: In Theory

An innovative approach uses a random-forest-based framework to link logging-while-drilling and multifrequencey seismic data to enable dynamic updates to lithology parameter predictions, enhancing efficiency and robustness of geosteering applications.

Best practices are not static; they evolve alongside advancements that redefine what is achievable.

This paper presents a fundamental research study with the main objective of building a mechanistic numerical model that captures the important mechanisms of polymer flooding through various mechanistic equations using a combined reservoir flow and geochemical numerical simulator.

This paper presents a continuous passive magnetic ranging technique that can provide real-time distance and direction to the offset well while drilling without interrupting drilling operations.

The authors of this paper describe a project to develop a virtual sensor to monitor the cooling effect downstream of a subsea choke to avoid hydrate plugs during cold-start operations.

In this study, a deep-neural-network-based workflow with enhanced efficiency and scalability is developed for solving complex history-matching problems.

This study presents a production-optimization method that uses a deep-learning-based proxy model for the prediction of state variables and well outputs to solve nonlinearly constrained optimization with geological uncertainty.

The paper describes a parameter inversion of reservoirs based on featured points, using a semi-iterative well-test-curve-matching approach that addresses problems of imbalanced inversion accuracy and efficiency.

Technology and partnerships play a pivotal role in how the oil industry finds and produces energy from frontier regions and brownfields, both now and in the future.

The authors propose a hybrid virtual flow and pressure metering algorithm that merges physics-based and machine-learning models for enhanced data collection.