TY - THES

T1 - Performance Screening of Conventional Hydrocarbon Reservoirs for Recovery Factor Maximization

AU - Török, Katalin

N1 - embargoed until 06-09-2021

PY - 2016

Y1 - 2016

N2 - The aim of this Master Thesis is to create and test a concept for a platform where petroleum companies or regulators can compare the production performance of their fields in a transparent and efficient way. The focus lays on the development of screening methods for recovery factor maximization. Efficiency of primary, secondary and tertiary recovery methods is assessed. The concept is built in a top-down approach from field level evaluation down to single well performance assessment. The calculations consist of conventional industrial screening methods combined with data mining practices. A risk and uncertainty concept is also set up where the result reliability grows with the amount and quality of the available data. In the screening process the logic identifies which portion of the original oil in place can be recovered during a certain recovery method. The computation of the complexity indices is carried out by utilizing Bayesian networks which enables probabilistic results. The proposed workflow is divided into two main parts: reservoir screening and production efficiency screening. The output of the first part is the static recovery reduction index, which reflects the expected ultimate recovery of the field based on the complexity of the reservoir architecture, reservoir structure, petrophysics, hydrocarbon properties and, in case of a fractured reservoir, the fracture network. The outcome of the production efficiency screening is the “man influence” index. It reflects the difference between the theoretically possible recovery factor and the predicted recovery factor at the end of a certain development stage. Furthermore, analysis is undertaken to identify the source of the resulted gap. The screening differentiates among water-related, gas-related wastage and inefficiency arising from the field development strategy. This part of the workflow is particularly important, because the here acknowledged losses can be minimized in the future by exploitation strategy optimization. The Norne field, provided by Statoil and the NTNU/IO CENTRE, is used for testing the above described concept. This oil field is situated offshore Norway and is on production since 1997. For the screening the reservoir is divided into two segments. The bottom segment is in the secondary development stage and the top segment is produced by primary recovery methods. The reservoir screening recognized for both segments relative low reservoir complexity scores. On the other hand, the performance screening identified production losses originating from a combination of all above mentioned wastage possibilities. As a conclusion, it can be stated that the proposed screening method serves the purpose to efficiently identify possible losses in the petroleum exploitation chain without the need of detailed modeling. Its main restriction is that it doesn’t yet account for surface and economic constraints. Further development of the overall workflow is planned to overcome the above mentioned limitations and to make the screening more generalized to be able to compare the performances of different reservoirs.

AB - The aim of this Master Thesis is to create and test a concept for a platform where petroleum companies or regulators can compare the production performance of their fields in a transparent and efficient way. The focus lays on the development of screening methods for recovery factor maximization. Efficiency of primary, secondary and tertiary recovery methods is assessed. The concept is built in a top-down approach from field level evaluation down to single well performance assessment. The calculations consist of conventional industrial screening methods combined with data mining practices. A risk and uncertainty concept is also set up where the result reliability grows with the amount and quality of the available data. In the screening process the logic identifies which portion of the original oil in place can be recovered during a certain recovery method. The computation of the complexity indices is carried out by utilizing Bayesian networks which enables probabilistic results. The proposed workflow is divided into two main parts: reservoir screening and production efficiency screening. The output of the first part is the static recovery reduction index, which reflects the expected ultimate recovery of the field based on the complexity of the reservoir architecture, reservoir structure, petrophysics, hydrocarbon properties and, in case of a fractured reservoir, the fracture network. The outcome of the production efficiency screening is the “man influence” index. It reflects the difference between the theoretically possible recovery factor and the predicted recovery factor at the end of a certain development stage. Furthermore, analysis is undertaken to identify the source of the resulted gap. The screening differentiates among water-related, gas-related wastage and inefficiency arising from the field development strategy. This part of the workflow is particularly important, because the here acknowledged losses can be minimized in the future by exploitation strategy optimization. The Norne field, provided by Statoil and the NTNU/IO CENTRE, is used for testing the above described concept. This oil field is situated offshore Norway and is on production since 1997. For the screening the reservoir is divided into two segments. The bottom segment is in the secondary development stage and the top segment is produced by primary recovery methods. The reservoir screening recognized for both segments relative low reservoir complexity scores. On the other hand, the performance screening identified production losses originating from a combination of all above mentioned wastage possibilities. As a conclusion, it can be stated that the proposed screening method serves the purpose to efficiently identify possible losses in the petroleum exploitation chain without the need of detailed modeling. Its main restriction is that it doesn’t yet account for surface and economic constraints. Further development of the overall workflow is planned to overcome the above mentioned limitations and to make the screening more generalized to be able to compare the performances of different reservoirs.

KW - Leistungsklassifizierung von Lagerstätten

KW - komplexe Indizes

KW - Bayesian Netzwerk

KW - Maximierung des Gewinnungsfaktors

KW - Produktionseffizienz

KW - reservoir screening

KW - production performance analysis

KW - complexity score

KW - recovery factor maximization

KW - bayesian network

M3 - Master's Thesis

ER -