Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.726850
Title: Enhancement of dynamic reservoir interpretation using the well2seis technique
Author: Yin, Zhen
ISNI:       0000 0004 6422 4441
Awarding Body: Heriot-Watt University
Current Institution: Heriot-Watt University
Date of Award: 2016
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Abstract:
The study in this thesis shows that dynamic reservoir interpretation can be enhanced by directly correlating the attributes from many repeated 4D seismic monitors to the production and injection behaviour of one or a group of wells. This 'well2seis' cross-correlation is achieved by defining a linear relationship between pressure and saturation-related 4D seismic responses and their corresponding changes in the cumulative fluid volumes at the wells. With a properly determined threshold, the spatial distribution of the well2seis correlation attribute can reveal key reservoir connectivity features, such as the seal of faults and intra-reservoir shale layers, fluid flows and pressure diffusion pathways, and communication between neighbouring compartments or fields. It is also shown that the enhanced interpretation from well2seis becomes more reliable when combined with the conventional "well2well" methods that are based on well production and injection rate fluctuations and bottom-hole pressure measurements. To appropriately make of use the well2seis interpretations, a workflow is proposed to close the loop between 4D seismic and reservoir engineering data. Firstly, the reservoir model is directly updated using the improved understanding of reservoir structure and connectivity. Seismic assisted history matching (AHM) is then performed to quantitatively use the well2seis attribute to honour data from both seismic and reservoir engineering domains, whilst simultaneously preserving the reservoir geology. Compared to traditional history matching approaches that attempt to match individual seismic time-lapse attributes and production observations, the proposed approach utilises the well2seis correlation attribute that condenses 4D seismic and production data. In addition, the approach is observed to improve the history matching efficiency as well as the reservoir model predictability. The proposed technique is firstly validated by synthetic field cases and then applied to the 4D seismic data from several fields. In its application to the fault-compartmentalised Heidrun field, the well2seis correlations are obtained as a consequence of the reservoir fluid communication and compartmentalisation across the compartments, while in Harding and Gryphon, the pressure communications between the neighbouring fields are revealed. The results of both applications suggest that the proposed well2seis technique can be an efficient tool for assessing intra- and inter- reservoir connectivity. The combination between well2seis and well2well is tested on the Norne field, demonstrating that the well injection and production fluctuations can assist in the selection of appropriate wells as input to the well2seis. This makes the 4D interpretation on Norne become more robust, detecting the pressure diffusion and fluid flow pathways consistently with bottom-hole pressure measurements and sea water production breakthrough observations. Furthermore, the field applications also identify key fault barriers which were not considered in the initial structure models. The quantitative use of the well2seis attribute is validated on another North Sea field for reservoir model updating. After the static model updating and assisted history matching, the desired loops are closed by efficiently updating the reservoir simulation model, and this is indicated by a 90% reduction in the misfit errors and 89% lowering of the corresponding uncertainty bounds. Overall, my studies indicate that the well2seis technique initiates a new direction to acquire insights into the dynamic reservoir changes by bridging the gap between 4D seismic and reservoir engineering.
Supervisor: MacBreth, Colin Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.726850  DOI: Not available
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