Maximizing Information through Data Driven Analytics in Petrophysical Evaluation of Well Logs
- Vikas Jain (Schlumberger) | Kais Gzara (Schlumberger) | Gennady Makarychev (Schlumberger) | Chanh Cao Minh (Schlumberger) | Denis Heliot (Schlumberger)
- Document ID
- Society of Petroleum Engineers
- SPE Annual Technical Conference and Exhibition, 28-30 September, Houston, Texas, USA
- Publication Date
- Document Type
- Conference Paper
- 2015. Society of Petroleum Engineers
- 5.2 Fluid Characterization, 7.6.4 Data Mining, 3 Production and Well Operations, 5.6.3 Deterministic Methods, 7.6 Information Management and Systems, 1.6.9 Coring, Fishing, 5.2.2 Fluid Modeling, Equations of State, 5 Reservoir Desciption & Dynamics, 7 Management and Information, 5.6 Formation Evaluation & Management, 1.6 Drilling Operations, 5.6.1 Open hole/cased hole log analysis
- Data Driven Analytics, Time lapse, Domain Analytics, Log Analysis, NMR
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Modern logging data are characterized by abundance and multiple dimensions: spatial, temporal, and physical. Traditional interpretation workflows for logging data are often limited in their scope and flexibility and use only a subset of the data dimensions, either by choice or necessity. Furthermore, the various deterministic or stochastic workflows typically rely on preconceived rock and fluid models. When those a priori models do not fit the data, confusion can ensue, whereas it is obvious that the correct model is buried somewhere in the "big logging data."
To overcome these limitations, new symbiotic approaches using domain expertise and data analytics ("domain-analytics") have been developed. Domain experts use those novel approaches to develop data-driven workflows to explore and mine complex logging datasets for latent but interpretable information. Once validated to be idempotent, the expert workflows that respect both the acquired data and domain knowledge can be packaged into new classes of answers products.
In this paper, the novel approaches, along with the examples of new answer products, are presented. These include 1) the automated spatial search for common modes and repeated patterns in the single or multi-well nuclear magnetic resonance (NMR) data, 2) the generation of a data-driven fluid model using time-lapse logging data acquired during multiple passes over the same formation, and 3) the creation of interpretive class-based models using definitive core data that could be propagated to continuous log data for a richer petrophysical interpretation.
|File Size||2 MB||Number of Pages||11|
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