SPE Journal

This paper presents the characterization and comparison of a metakaolin-based geopolymer as a candidate treatment for remedial operations in oil and gas wells versus conventional state-of-the-art materials.

This paper presents the development of a robust, physics-based, and data-driven workflow for modeling mud loss in fractured formations and predicting terminal mud loss volume and time, as well as equivalent hydraulic fracture aperture.

This comprehensive study on microbial control in unconventional facilities allows for the integration of molecular microbiology, chemical treatments, and production engineering to develop specific strategies to control microbial communities and reduce corrosion rates that affect the integrity of the facilities.

This paper studies the effect of salinity and hardness on partially hydrolyzed polyacrylamide rheology in sandstones with relevance to polymer flooding models and simulations.

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.

This paper presents a comprehensive literature review of perforate, wash, and cement techniques that compares new methods with traditional ones and uses field cases and computational fluid dynamics to find the most cost- and time-effective practices without sacrificing safety.

This study combines preshear degradation, permeability, and oil presence effects to evaluate and improve polymer injectivity using acrylamido tertiary butyl sulfonate (ATBS) polymer in carbonate rock.

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 comprehensive review of stuck pipe prediction methods focuses on data frequency, approach to variable selection, types of predictive models, interpretability, and performance assessment with the aim of providing improved guidelines for prediction that can be extended to other drilling abnormalities, such as lost circulation and drilling dysfunctions.

This paper introduces a novel optimization framework to address CO2 injection strategies under geomechanical risks using a Fourier neural operator-based deep-learning model.