DSDE: In Practice

This paper presents a comprehensive technical review of applications of distributed acoustic sensing.

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 site survey and inspection of an offshore gas platform in UAE waters was executed entirely from an onshore remote operations center without sending personnel offshore.

The authors of this paper describe a solution using machine-learning techniques to predict sandstone distribution and, to some extent, automate the process of optimizing well placement.

This paper introduces a method using a Bayesian network to aggregate trends detected in time-series data with events identified by natural language processing to improve the accuracy and robustness of kick and lost-circulation detection.

The objective of this study was to establish an efficient optimization work flow to improve vertical and areal sweep in a sour-gas injection operation, thereby maximizing recovery under operation constraints.

This study illustrates how a combination of a straddle-packer system and a downhole real-time telemetry successfully stimulated up to 38 stages while maintaining packer-seal integrity and downhole pressure.

This paper presents an integrated system for fracturing optimization using real-time and historical data along with organizational knowledge and the challenges and key considerations of implementing such a system.

This paper presents the interpretation of two polymer-injectivity tests performed in two giant light-oil high-salinity/high-temperature carbonate reservoirs onshore Abu Dhabi.