Massive Optimization Technique Improves Production of Mature Fields: San Francisco, Colombia
- Andres Felipe Suarez | David Soto (Hocol S.A.) | Hubert de J. Borja (Hocol S.A.) | Remi Yves Daudin (ForOil Group SAS)
- Document ID
- Society of Petroleum Engineers
- SPE Latin American and Caribbean Petroleum Engineering Conference, 1-3 December, Lima, Peru
- Publication Date
- Document Type
- Conference Paper
- 2010. Society of Petroleum Engineers
- 4.3.4 Scale, 5.6.3 Deterministic Methods, 1.2.3 Rock properties, 1.6 Drilling Operations, 6.5.2 Water use, produced water discharge and disposal, 2.4.3 Sand/Solids Control, 5.5.8 History Matching, 5.4.1 Waterflooding, 4.1.2 Separation and Treating, 5.2.1 Phase Behavior and PVT Measurements
- 1 in the last 30 days
- 503 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||USD 8.50|
|SPE Non-Member Price:||USD 25.00|
A breakthrough methodology has been implemented in the San Francisco field, Colombia, to yield an additional 12% in remaining reserves by optimizing production and injection based on key principles of the ´learning theory´.
This paper presents a new massive optimization methodology applied in the San Francisco field. Such techniques are especially well suited to mature fields, as these have substantial infrastructure in place, which can be used in ways different to the usual.
Mature fields represent a challenge in terms of investment and allocation of resources. Still, there remain opportunities to improve production, since past strategic choices have created some heterogeneity in field pressure and saturation. These can be drastically revisited and production parameters rearranged. Since many production avenues have been explored in the past, a learning process can be applied: rearranged parameters can be implemented with very low risk. Still, there are billions of ways to operate mature fields: injection and production rates can be varied, conversions made.
Massive optimization of a field means selecting and playing with thousands of production scenarios and identifying the best. This is possible only with a reliable and fast simulator. The "statistical learning theory?? defines under which conditions such a simulator can be devised. The Production Simulator is designed in such a way that it complies with the conditions of a reliable long-term forecast.
This new approach has been successfully implemented in the San Francisco field in order to re-organize the water-flooding regime, in particular through the conversion of producers into injectors. The entire process took only three months.
Our methodology has led to an expected additional production of 1.2 mmbo, a +12% increase in remaining reserves, with a total development cost of 4.5 US$/bbl. Current production data, one year after implementation, is in line with the forecast.
The San Francisco field was discovered in 1985 in Colombia (Figure 1) and has been producing from the Caballos formation. This is a highly heterogeneous reservoir in a fractured anticlinal, 3000 feet deep. The initial pressure was 1100 psia with an approximate bubble point pressure of 950 psia and oil quality varying from 23° to 28° degrees API. The field has been under a waterflood scheme since 1989. The current total fluid production is 250,000 bbl with an average water cut of 96.7%.
By 2008, the operator, Hocol S.A., had drilled a total of 193 wells consisting of 128 active producers and 76 active injectors with varied results. Due to the heterogeneity of the field, complicated by dense faulting and water injection under an unfavorable mobility ratio, the operator needed to determine the candidates for the upcoming conversion campaign; not only the location of future wells but also what injection and production rates to implement.
|File Size||857 KB||Number of Pages||11|