Summary Brownfields in this paper are defined as mature fields where production declined to less than 35–40% of the plateau rate and where primary and secondary reserves have been largely depleted. Big data, high field complexity after a long production history, and slim economic margins are typical brownfield challenges. In the exploration-and-production (E&P) industry, “sequential” field-evaluation approaches (first “static,” then “dynamic”), have proved successful for greenfield development, but often do not achieve satisfying results for brownfields. This paper presents a new work flow for brownfield re-evaluation and rejuvenation. The “reversed” geo-dynamic field modeling (GDFM) rearranges existing elements of reservoir evaluation to obtain a purpose-driven, deterministic reservoir model, which can be quickly translated into development scenarios. The GDFM work flow is novel because (1) it turns upside down the discipline-driven sequential work flow (i.e., starts with the history match) and (2) it uses dynamic data as input to calibrate seismic (re-) interpretation that acts as a main integration step. It combines all available data already during horizon and fault mapping. Field diagnosis, flow-unit identification, well-test reanalysis, and petrophysical and geological interpretations are all combined in a cross-discipline interaction to guarantee data consistency. This directly ensures a fully integrated, “geo-dynamic” model that forms the basis for reservoir modeling. The full dynamic/static data coupling at an early stage is the main strength of the GDFM. It reduces the model complexity, and narrows the uncertainties. Project-execution time is considerably shortened by avoidance of the characteristic full-cycle loop iterations of the sequential approaches. A brownfield example illustrates the benefits of GDFM: a consistent history match with high model accuracy and confidence. In the field example, the GDFM work flow has facilitated a turnover at only 70% of the original time budget. The ongoing drilling has confirmed model validity (“attic oil” predictions), thus further postponing the economic limit of the brownfield.

Brownfields are typically characterized by enormous amounts of data that include various technologies and disciplines enabling highly detailed and probabilistic models. However, data abundance often puts a threat to evaluation effectivity and to a correct identification of key controlling factors. In the E&P industry, many "sequential" approaches exist for mature field evaluation, where "static" and "dynamic" data are disconnected during the process. The main objective of this paper is to present a new and fully integrated workflow for brownfield re-evaluation and rejuvenation. Our "reversed" Geo-Dynamic Field Modelling (GDFM) relies on simultaneous cross-discipline interaction to guarantee data consistency and is based on a full dynamic/static data coupling at a very early stage. The GDFM workflow reverses key elements of the discipline driven sequential workflow. Production and pressure data analysis comes first, not last. Field diagnosis, identification of flow units, well test re-analysis, petrophysical and geological re-interpretation are combined in cross-disciplines model constraints. These include identification of major field issues, data reliability rankings, uncertainty/certainty and impact analysis, parameter trends etc. Seismic (re−) interpretation acts as the first main integration step, honoring all available data already during horizon and fault mapping. This directly ensures a fully integrated model that – combined with seismic attribute analysis - forms the basis for static reservoir modelling and dynamic simulation. A field example illustrates the benefits of the GDFM: a consistent history match at considerably reduced iteration time while focusing on the key controlling factors. GDFM increases model accuracy and confidence because all data sources are incorporated, honored and cross-discplinary quality controlled early in the process. The applied workflow supports uncertainty/certainty analysis for history matching, enhances the geologic model, improves the reservoir properties distributions and presents a solid base for the dynamic simulation. Though a number of minor iterations between the main work packages are still required, the case study shows that full cycle repetitions between static and dynamic modelling are avoided, which considerably reduces the time requirements of the subsurface re-evaluation and improves remarkably the studiy results.