Data Management and Communication
The Petroleum Domain Practitioner—A Multiscience Talented Professional
The pursuit of excellence in the upstream petroleum area in the last 3 decades has been supported by rapidly changing digital-information technologies as well as by classical sciences such as math, thermodynamics, and transport phenomena; however, interestingly, it has been reinforced by data-driven sciences such as descriptive statistics, automatic-process-control theory, data mining, and predictive-analytics-modeling techniques. The latter have been referred to as artificial-intelligence techniques as they are incorporated into the oil field. Data-driven knowledge-search case histories proliferate throughout the petroleum (and other) literature. Many of those have clear value, self-learning tutorials, and simple and clear implementation paths; others are far from practical.
The development and production of oil and gas resources supported by more-available continuous real-time data sources entail incessant learning and knowledge search over a relatively long period of time. As we drill more wells, as we produce for various decades and increasingly acquire more data and information, uncertainty about the success of new wells is progressively reduced and production forecasts and economic value of oil and gas fields are continuously improved.
Fundamental-Physics vs. Data-Driven Models
Because fundamental-physics models intend to prompt us about the thermodynamic principles that hold data together, engineers identify and characterize the fundamental modeling elements affecting petroleum production forecasts in a multidisciplinary fashion and, at the same time, analyze existing and new data on the basis of cross-validation approaches and dimensionality-reduction modeling techniques to uncover patterns that would be useful for making predictions on production and recovery.
A hybrid of these approaches would provide the most promising approach to oilfield-knowledge management. The fundamental-physics approach would propose the structure of the models, and data analysis would refine possible model structures and determine values or related parameters so robust predictions can be made.
For example, time and spatial dependency are important factors in the resolution of uncertainty associated with oil and gas occurrence and performance. Therefore, hybrid approaches considering both data-driven modeling and physics modeling that considers fundamental analytic relations of time and space seem to be reasonable approaches. The multiple time and space scales we manage in the oil field require further advances in how data are validated and interpreted and, most important, how results are communicated fast enough to make the right decisions at the right time.
The following selected case studies demonstrate how these hybrid approaches can be used to improve our geological knowledge as more wells are drilled (SPE 164816), how to uncover patterns to increase recovery as new key events in data are interpreted (SPE 167836), and how to optimize drilling as live data are analyzed (OTC 25164).
This Month's Technical Papers
Recommended Additional Reading
SPE 164465 Improved Permeability Prediction From Seismic and Log Data Using Artificial-Intelligence Techniques by Fatai A. Anifowose, King Fahd University of Petroleum and Minerals, et al.
SPE 167398 Automated Work Flows To Monitor, Diagnose, Optimize, and Perform Multiscenario Forecasts of Waterflooding in Low-Permeability Carbonate Reservoirs by P. Ranjan, Halliburton, et al.
SPE 167399 Multivariate Analysis of Job-Pause-Time Data Using Classification and Regression Tree and Kernel Clustering by Marko Maučec, Halliburton, et al.
SPE 167839 Advanced Machine-Learning Methods for Production-Data-Pattern Recognition by Niranjan Subrahmanya, ExxonMobil Research and Engineering Company, et al.
Data Management and Communication
Luigi Saputelli, SPE, Frontender Corporation
01 October 2014
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03 July 2019