A New Decline-Curve-Analysis Method for Layered Reservoirs
- Kittiphong Jongkittinarukorn (Chulalongkorn University) | Nick Last (Well Test Knowledge International) | Freddy Humberto Escobar (Universidad Surcolombiana) | Kreangkrai Maneeintr (Chulalongkorn University)
- Document ID
- Society of Petroleum Engineers
- SPE Journal
- Publication Date
- March 2020
- Document Type
- Journal Paper
- 2020.Society of Petroleum Engineers
- commingled production, multilayer reservoir, decline curve analysis
- 12 in the last 30 days
- 76 since 2007
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This study presents a new method to improve production forecasts and reserve estimation for a multilayer well in the early stages of production using the Arps (1945) hyperbolic decline method to model the decline rate of each layer. The method can be applied to both oil and gas wells.
The new approach generates the profiles of the instantaneous decline rate (D) and instantaneous decline-curve exponent (b) from the historical flow rate (q). Because of the inherent noise in the production data, a regression technique is applied to smooth the flow-rate data, and the analysis is performed on the smoothed data. History matching is performed not only on the profile of q but also on the profiles of D and b. This results in the unique decline parameters (qi, Di, and b) for each layer.
For a multilayer well, the values of D and b vary with time, which means that the well’s performance cannot be modeled using a conventional single-layer-well approach. Furthermore, the well-known nonuniqueness problem from history matching is magnified in a multilayer well: Many models can successfully match the production profile in the short-term but fail to match it in the longer term. Only the correct model can match the profiles of q, D, and b over both the short-term and the long-term. The proposed method provides the correct unique decline parameters (qi, Di, and b) for each layer, during the early stages of production, and these parameters are then valid for the life of the well. The method works well for both synthetic examples and actual field data.
The novelty of the new methodology is the ability to provide the decline parameters for each layer at an early stages of production that can then be used for production forecasting in the long-term. The nonuniqueness problem from history matching is solved.
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