Selection of EOR/IOR Opportunities Based on Machine Learning
- Vladimir Alvarado (PDVSA Intevep) | Aaron Ranson (PDVSA Intevep) | Karen Hernandez (PDVSA Intevep) | Eduardo Manrique (PDVSA Intevep) | Justo Matheus (PDVSA Intevep) | Tamara Liscano (FUNDATEC) | Natasha Prosperi (PDVSA Intevep)
- Document ID
- Society of Petroleum Engineers
- European Petroleum Conference, 29-31 October, Aberdeen, United Kingdom
- Publication Date
- Document Type
- Conference Paper
- 2002. Society of Petroleum Engineers
- 5.8.9 HP/HT reservoirs, 5.4 Enhanced Recovery, 7.6.6 Artificial Intelligence, 5.1 Reservoir Characterisation, 5.6.4 Drillstem/Well Testing, 5.8.8 Gas-condensate reservoirs, 2.5.2 Fracturing Materials (Fluids, Proppant), 5.6.5 Tracers, 5.4.6 Thermal Methods, 1.6 Drilling Operations, 5.7.2 Recovery Factors, 2.4.3 Sand/Solids Control, 5.6.1 Open hole/cased hole log analysis, 5.2.1 Phase Behavior and PVT Measurements, 4.3.4 Scale, 5.4.1 Waterflooding, 5.4.7 Chemical Flooding Methods (e.g., Polymer, Solvent, Nitrogen, Immiscible CO2, Surfactant, Vapex), 5.4.2 Gas Injection Methods, 4.1.9 Heavy Oil Upgrading, 5.7.5 Economic Evaluations, 5.6.9 Production Forecasting, 7.6.4 Data Mining
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The Venezuelan National Oil Company, PDVSA, has dedicated a sustained effort to adapt EOR/IOR technologies to rejuvenate a large number of its mature fields. The first step towards achieving this objective was to select cost-effective technologies suited for conditions of Venezuelan reservoirs. The current strategy for screening EOR/IOR applications is based on the Integrated Field Laboratory philosophy, where a representative pilot area of a number of reservoirs is selected to intensively test EOR/IOR methods, such as WAG injection (water alternating gas) and ASP (alkali polymer surfactant), currently underway. Two problems with this approach are the lack of objective rules to define a reservoir type and the project completion time. In general, the trouble with using expert opinion is that it tends to be too biased by operational experience. It is known that the success of a given EOR/IOR method depends on a large number of variables that characterize a given reservoir. Therefore, the main difficulty for selecting an adequate method is to determine a relationship between reservoir characteristics and the potential of an EOR/IOR method. In this work, data from worldwide field cases have been gathered and data mining was used to extract the experience on those fields. Here, a space reduction method has been used to facilitate the visualization of the needed relationship. Machine learning algorithms have been utilized to draw rules for screening. To illustrate the procedure, several Venezuelan reservoirs have been mapped onto the extracted representation of the international database.
Primary and secondary recovery methods generally result in recoverable reserves between 40 and 50%. The latter depends on reservoir complexity and reservoir conditions, field exploitation strategy and is greatly affected by economics. Tertiary recovery or Improved Oil Recovery (IOR) methods are key processes to replace or upgrade reserves, which can be economically recovered, beyond conventional methods. Therefore, the application of IOR methods offers opportunities to replace hydrocarbon reserves that have been produced in addition to those coming from exploration and reservoir appraisal1,2. In this work, we concentrate on screening of EOR processes, rather than IOR, but no real limitation for the method presented here is known at the present time.
PDVSA, the Venezuelan National Oil Company, holds a long history of oil and gas production, with all its E & P assets located in Venezuela. This history brings along a large number of mature, near abandonment, reservoirs. PDVSA operates a variety of accumulations, most of them in sandstone formations, with wide spread in API gravity, from bitumen and heavy oils, to volatile oil and condensate reservoirs. Exploitation plans have often yielded low recovery factors, that in average amount to 30% for waterflooding and 40% for gas injection, and lower values for primary recovery in most cases.
One of the major difficulties to manage such a portfolio of opportunities relates to numerous reservoirs under dissimilar conditions and the long list of Enhanced Oil Recovery (EOR) technologies available. As expected, screening/ranking of these processes can become a daunting task. Two constraints limit the use traditional evaluation techniques in PDVSA's case:
Maturing reservoirs have short life span, hence time is quite limited for the decision-making process.
Reservoir characterization is far from complete in a large portion of the portfolio. Although integrated studies are underway, many reservoirs lack enough financial performance to justify information or data gathering.
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