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Forward stratigraphic modelling is based on the deterministic reconstruction of depositional processes in a sequence of time steps moving forward in time. This approach is usually hindered with various, uncertain parameters. Today, uncertainty analysis using experimental design and response surface techniques is commonly used in the field of dynamic reservoir simulation. This study presents the innovative application of these techniques on forward stratigraphic modelling of a giant carbonate field from offshore Abu Dhabi, leading to the generation of multiple realizations to be used as the starting point for better geomodel construction.A variety of environmental and stratigraphic parameters are used, some of which carry an important uncertainty with regards to their range of possible values. It is therefore critical to assess their impact on the development of the basin fill -a tedious exercise for subsurface fields, whereby the only physical data come from well cores. The Experimental Design and Response Surface techniques have been innovatively applied at reservoir scale to improve the calibration of the model and to produce alternative facies distribution scenarios in the study reservoir.The idea behind this approach is, first to perform a global sensitivity analysis with a large number of parameters and simulations, and then to narrow down the uncertain domain in order to select the best stratigraphic models according to criteria of calibration quality and geological consistency. Input data for this model calibration consisted mostly of an extensive sedimentological core study carried out on several wells, and a high resolution sequence stratigraphic analysis. The quality of calibration (simulation vs core data) was assessed by two user-defined quantitative functions called Thickness Calibration Indicator and Rock Texture Calibration Indicator.Following a first manual calibration of a reference case, specific uncertain parameters (e.g. eustasy; carbonate production versus depth; carbonate production vs. time; wave parameters; gravity and wave transport; erosion rates) were selected and their ranges of values defined based on experience and knowledge of geology over the study area. Latin Hypercube Experimental Design was used to ensure a uniformly distributed sampling of the parameters. Sensitivity analysis based on the responses (texture and thickness calibration indicators) was carried out and allowed to identify the most influential parameters as well as their ranges of values yielding good calibration indicator values.A second set of simulations was then launched considering only the most influential parameters and their refined ranges. Other parameters were assigned with constant values used in the reference case model. This generated a collection of various, well calibrated models. A last filtering of simulations with the highest calibration indicator values and good geological consistency was performed to provide a handful of acceptable multi-realizations. Finally, confidence maps were computed based o...
Forward stratigraphic modelling is based on the deterministic reconstruction of depositional processes in a sequence of time steps moving forward in time. This approach is usually hindered with various, uncertain parameters. Today, uncertainty analysis using experimental design and response surface techniques is commonly used in the field of dynamic reservoir simulation. This study presents the innovative application of these techniques on forward stratigraphic modelling of a giant carbonate field from offshore Abu Dhabi, leading to the generation of multiple realizations to be used as the starting point for better geomodel construction.A variety of environmental and stratigraphic parameters are used, some of which carry an important uncertainty with regards to their range of possible values. It is therefore critical to assess their impact on the development of the basin fill -a tedious exercise for subsurface fields, whereby the only physical data come from well cores. The Experimental Design and Response Surface techniques have been innovatively applied at reservoir scale to improve the calibration of the model and to produce alternative facies distribution scenarios in the study reservoir.The idea behind this approach is, first to perform a global sensitivity analysis with a large number of parameters and simulations, and then to narrow down the uncertain domain in order to select the best stratigraphic models according to criteria of calibration quality and geological consistency. Input data for this model calibration consisted mostly of an extensive sedimentological core study carried out on several wells, and a high resolution sequence stratigraphic analysis. The quality of calibration (simulation vs core data) was assessed by two user-defined quantitative functions called Thickness Calibration Indicator and Rock Texture Calibration Indicator.Following a first manual calibration of a reference case, specific uncertain parameters (e.g. eustasy; carbonate production versus depth; carbonate production vs. time; wave parameters; gravity and wave transport; erosion rates) were selected and their ranges of values defined based on experience and knowledge of geology over the study area. Latin Hypercube Experimental Design was used to ensure a uniformly distributed sampling of the parameters. Sensitivity analysis based on the responses (texture and thickness calibration indicators) was carried out and allowed to identify the most influential parameters as well as their ranges of values yielding good calibration indicator values.A second set of simulations was then launched considering only the most influential parameters and their refined ranges. Other parameters were assigned with constant values used in the reference case model. This generated a collection of various, well calibrated models. A last filtering of simulations with the highest calibration indicator values and good geological consistency was performed to provide a handful of acceptable multi-realizations. Finally, confidence maps were computed based o...
This paper discusses the re-construction of the long-term development plan for an offshore giantfield located in Abu Dhabi with the aim to mitigate the rising challenges in the maturing field. The primary objective is to understand the reservoir behavior in terms of fluid movement incorporating the learning from the vast history while correlating with the geological features. The field has been divided into segments based on multiple factors considering the static properties such as facies distribution, diagenesis, faults, and fractures while incorporating the dynamic behaviors including pressure trends and fluid movements. On further analysis, various trends have been identified relating these static and dynamic behaviors. The production mechanism for each of the reservoirs and the subsequent sub reservoirs were analyzed with the help of Chan plots, Hall plots and Lorentz plots which distinctly revealed trends that further helped to classify the wells into different production categories. Using the above methodology the field has been categorized in segments and color coded to indicate areas of different ranking. The green zone indicates area of best interest which currently has strong pressure support and wells can be planned immediately. The wells in this area are expected to produce with a low risk of water and gas. The yellow zone indicates areas of caution where special wells including smart wells maybe required to sustain production. This area showed relatively lower pressure support owing the location of the water injectors and the degraded facies quality between the injectors and the producers. The red zone highlights areas which are relatively mature compared to the neighboring zones and will require new development philosophy to improve the recovery. The findings from this study were used as the basis for a reservoir simulation study using a history matched model, to plan future activities and improve the field recovery. This study involved an in-depth analysis incorporating the latest findings with respect to the static and dynamic properties of the reservoir. This has helped to classify the reservoir based on the development needs and will play a critical role in designing the future strategies in less time.
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