Oilfield Data Mining Workflows for Robust Reservoir Characterization: Part 2
- Keith Richard Holdaway (SAS Institute Inc.)
- Document ID
- Society of Petroleum Engineers
- SPE Intelligent Energy International, 27-29 March, Utrecht, The Netherlands
- Publication Date
- Document Type
- Conference Paper
- 2012. Society of Petroleum Engineers
- 5.1.8 Seismic Modelling, 5.8.3 Coal Seam Gas, 5.8.1 Tight Gas, 4.1.5 Processing Equipment, 2 Well Completion, 5.1.7 Seismic Processing and Interpretation, 4.6.2 Liquified Natural Gas (LNG), 5.1 Reservoir Characterisation, 4.3.4 Scale, 4.6 Natural Gas, 5.8.7 Carbonate Reservoir, 5.7.2 Recovery Factors, 7.6.6 Artificial Intelligence, 1.6 Drilling Operations, 3 Production and Well Operations, 4.1.2 Separation and Treating, 5.1.1 Exploration, Development, Structural Geology, 3.2.3 Hydraulic Fracturing Design, Implementation and Optimisation, 5.6.9 Production Forecasting, 7.6.2 Data Integration, 5.6.3 Deterministic Methods, 7.6.4 Data Mining, 6.1.5 Human Resources, Competence and Training
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"There are more things in heaven and earth, Horatio, than are thought of in your philosophy"
"Hamlet" William Shakespeare
To fully appreciate the plethora of disparate data sets across the upstream geoscientific silos, it is essential to establish a sound and consistent suite of workflows that embrace data management facets as well as diverse data mining modules established under the banner of soft computing or artificial intelligence. When does one implement a neural network, a decision tree or non-linear regression techniques? Will Genetic Algorithms or Fuzzy Logic be appropriate for my objective function?
This paper sets out to answer some important questions around data mining workflows underpinned by exploratory data analysis, confirmatory data analysis, descriptive and predictive modeling to establish sound and important reservoir characterization decision-cycles. Case studies are presented to illustrate effective and successful studies based on advanced statistical analysis and AI workflows in sandstone and carbonate reservoirs. Can such workflows be adopted in unconventional reservoirs? Determining accurate and effective hydraulic fracturing packages are keys in tight gas plays, and this paper explicates how data mining workflows can be successfully applied in such unconventional plays.
We find ourselves in the midst of a data explosion, and those raw data are sourced across a multi-disciplinary environment in the petroleum industry. To master complex problems inherent in heterogeneous subsurface reservoir systems, we must break down the walls built around the traditional disciplines of petroleum engineering, geophysics, geology, petrophysics and geochemistry. Professional curiosity is trumped by necessity to uncover the knowledge hidden across all upstream data sets as a multi-disciplinary analysis, underpinned by a multivariate suite of advanced analytical workflows, is implemented through data mining methodologies. Thus we are required to complement the conventional wisdom of interpretation and deterministic approaches steeped in first principals with the emergence of soft computing techniques. It is important to grasp that the evolution of soft computing methodologies is based on the robust and tractable premise that unlike hard computing it is tolerant of uncertainties, imprecisions and partial truths. Intelligent reservoir characterization depends on the paradigm shift that is requisite to realize more profound understanding of complex subsurface systems; moving from the empirical sciences, the theoretical and computational to data mining and non-trivial extraction of implicit, previously unknown and potentially useful information from raw data. Artificial intelligence that embraces a menu of genetic algorithms, fuzzy logic and advanced data mining techniques morphs to a Platonic or inductive reasoning by pursuing a pathway from the general to the specific. Statistics too operate on patterns within the data, but tend to match pre-determined patterns to data in a deductive or Aristotelian way moving from the details to the general, big picture perspective.
|File Size||993 KB||Number of Pages||14|