Capillary Pressure: Is It Critical for Investment Decisions? Case Study of Gas Condensate Reservoir Underlain with Water
- Maksim F. Fayzullin (Novatek NTC) | Artur Nasibullin (Novatek Inc.) | Alexey V. Yazkov | Sergey Valentine Kolbikov (Novatek Inc)
- Document ID
- Society of Petroleum Engineers
- SPE Europec/EAGE Annual Conference, 4-7 June, Copenhagen, Denmark
- Publication Date
- Document Type
- Conference Paper
- 2012. Society of Petroleum Engineers
- 1.6 Drilling Operations, 5.2 Reservoir Fluid Dynamics, 5.5.3 Scaling Methods, 1.2.3 Rock properties, 4.1.2 Separation and Treating, 1.6.9 Coring, Fishing, 5.5.11 Formation Testing (e.g., Wireline, LWD), 5.5.2 Core Analysis, 5.4.2 Gas Injection Methods, 7.3.3 Project Management, 4.1.5 Processing Equipment, 5.1 Reservoir Characterisation, 5.8.8 Gas-condensate reservoirs, 5.6.4 Drillstem/Well Testing, 5.2.1 Phase Behavior and PVT Measurements, 5.6.1 Open hole/cased hole log analysis, 5.1.5 Geologic Modeling, 5.6.9 Production Forecasting, 5.5 Reservoir Simulation, 5.6.2 Core Analysis
- 0 in the last 30 days
- 390 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||USD 9.50|
|SPE Non-Member Price:||USD 28.00|
The paper highlights the importance of adequate characterization of capillary pressure effects when preparing a development plan for a greenfield gas condensate reservoir with a large transition zone (TZ).
Capillary pressure data from centrifuge or porous plate (semi-permeable membrane) are used to characterise the transition zone. It is essential that a representative set of sample measurements is obtained. Core laboratories are not capable to keep initial pressure-temperature conditions during capillary pressure measurements. Hence, the conversion from surface to reservoir becomes uncertain. Conversion utilizes interfacial tension and wettability angle which are quite unknown and can be predicted using different P-T charts. Finally saturation model depends on the way of: characterization - discrete Rock Types (RT) or tuned-up Continuous Functions (Leverett, Amaefule etc.); matching log saturation profile with the one observed in the model; welltest playback in terms of mobile water and drained volumes.
In this study, the authors present a systematic workflow on how capillary pressure should be incorporated in a dynamic simulation model pointing out example pitfalls and giving validation tips. The illustrated case shows that if one of the steps is missed or wrong assumptions are made, then the TZ and the production potential will be incorrect. In our example, the discretization of connate water saturation and capillary pressure curves on early stages resulted to 8% underestimation of GIIP. Moreover, results indicated that uncertainty in conversion of capillary curves (from surface to reservoir) gives 15-20% differences in outcomes (depending on development scenario). Also it demonstrates a strong impact on the length of production plateau, rate of wellhead pressure decline, compression start-up which are vital aspects for the development concept, especially during front-end-loading stage of the project plan. We feel that the procedures presented here (both for engineers and management) can serve as a guide for QC and possible failures when they are not applied.
Today's reservoir engineer possesses numerous powerful tools for the hydrocarbon production forecasts. One of them is the numerical reservoir simulation, which requires analyzing a huge amount of data: from the laboratory core studies, well log interpretation to the well test results and production history. The current advances in natural science do not allow for the simulation of many physical processes ab initio, i.e. on the basis of the fundamental principles. Therefore, an engineer actively applies phenomenological description of the physical processes without detailed understanding at the "low-level??. As a result, the engineer has a lot of degrees of freedom for systematization/ generalization of all available information to integrate it into a simulation model. Wrong choice of the phenomenological mathematical model (often it has empirical nature) and/or erroneous justification of its options can result in invalid investment decisions. Consequently it is very important to lay out strong system of the model validation.
|File Size||4 MB||Number of Pages||11|