Wolfcamp formation

This study reveals how production-induced depletion and geomechanical stress changes influence child-well performance in the Midland Basin, combining coupled simulations and machine learning to guide optimal well spacing, timing, and placement for infill development.

This paper investigates condensate-banking effects on well performance by conducting field-modeling studies on Delaware Basin deep Wolfcamp condensate producers using compositional simulation models with hydraulic fractures.

New and evolving artificial lift technology is helping operators improve production rates.

This paper details a successful pilot to improve long-term well performance using acid stimulation aided by a tailored metal oxide nanoparticle-based fluid in the Wolfcamp A formation.

A 44-well development tests what ConocoPhillips has learned about maximizing the value of the wells by figuring out how they drain the reservoir.

The authors of this paper analyze a robust, well-distributed parent/child well data set using a combination of available empirical data and numerical simulation outputs to develop a predictive machine-learning model.

In this paper, example machine-learning models were trained using geologic, completion, and spacing parameters to predict production across the primary developed formations within the Midland Basin.

The authors of this paper aim to identify the effect of various geologic controls on hydrocarbon maturation in the Delaware Basin by restoring regional cross sections and performing simulations of organic-matter maturity.

This paper presents a work flow that has been applied to crossdipole sonic data acquired in a vertical pilot well drilled in the Permian Basin.

Present industry solutions to the challenge of well spacing involve expensive geomechanical Earth modeling or fracture-geometry monitoring that is time-consuming, data-intensive, and geography-specific.