Abstract
Pressure gauges provide continuous well measurements during injection or production in a variety of industries, including the oil and gas, geological CO2 storage and geothermal industries. One of the crucial issues in processing the pressure measurements is transient identification, i.e. dividing the measurement history in sequential transients associated with rate variations. A common approach for transient identification is a continuous trial-test cycle until the end-user achieves the desired results, which is usually a difficult manual process. Automated transient identification is seen as a viable solution to tackle these difficulties and for better control in reservoir operations.
This paper introduces a new method, called TPMR (Topographic Prominence Max Rotation), for automated transient identification focused on shut-in periods. The method uses only pressure data from gauges, while rate data are used only to verify the results. In contrast with common industry practice, the TPMR method identifies shut-in transients without data pre-processing and manual threshold selection, reducing human interaction to a minimum. The paper focuses on testing the TPMR method with real datasets from water injection wells in the North Sea. The testing has demonstrated the capabilities and high accuracy of the TPMR method for automated shut-in transient identification for given datasets.
This paper introduces a new method, called TPMR (Topographic Prominence Max Rotation), for automated transient identification focused on shut-in periods. The method uses only pressure data from gauges, while rate data are used only to verify the results. In contrast with common industry practice, the TPMR method identifies shut-in transients without data pre-processing and manual threshold selection, reducing human interaction to a minimum. The paper focuses on testing the TPMR method with real datasets from water injection wells in the North Sea. The testing has demonstrated the capabilities and high accuracy of the TPMR method for automated shut-in transient identification for given datasets.
Original language | English |
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Pages | 1-5 |
Number of pages | 5 |
DOIs | |
Publication status | Published - 5 Jun 2023 |
Event | 84th EAGE Annual Conference & Exhibition 2023 - Vienna, Austria Duration: 5 Jun 2023 → 8 Jun 2023 |
Conference
Conference | 84th EAGE Annual Conference & Exhibition 2023 |
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Country/Territory | Austria |
City | Vienna |
Period | 5/06/23 → 8/06/23 |