modeling

Virtual reality and related visualization technologies are helping reshape how the industry views 3D data, makes decisions, and trains personnel.

The objective of this study is to develop an explainable data-driven method using five different methods to create a model using a multidimensional data set with more than 700 rows of data for predicting minimum miscibility pressure.

The authors present an open-source framework for the development and evaluation of machine-learning-assisted data-driven models of CO₂ enhanced oil recovery processes to predict oil production and CO₂ retention.

The authors of this paper propose hybrid models, combining machine learning and a physics-based approach, for rapid production forecasting and reservoir-connectivity characterization using routine injection or production and pressure data.

This paper analyzes the interaction of high-frequency torsional oscillations (HFTO) with lateral vibrations based on a model that accounts for the superimposed movement of whirl and HFTO at the bit.

This paper describes a collaborative project to analyze affected wells, identify commonalities, and optimize bit design and drilling parameters to mitigate the effects of borehole spiraling.

In this paper, bottomhole-assembly lateral behavior is analyzed using different types of computations, including static, dynamic, frequency-based, and time-based.

At SPE’s Permian Basin Energy Conference, operators shared behind-the-scenes details on innovations such as drilling horseshoe wells and trimulfrac completions along with in-basin challenges such as handling produced water.

This paper presents a specialized workflow that aims to quantify the severity of condensate banking and subsequently optimize reservoir development strategies for a deep formation in the Permian Basin.

This paper describes numerical modeling studies of fracture-driven interactions using a coupled hydraulic-fracturing-propagation, reservoir-flow, and geomechanics tool.