Bigoni, Francesco (Eni S.p.A) | Pirrone, Marco (Eni S.p.A) | Trombin, Gianluca (Eni S.p.A) | Vinci, Fabio Francesco (Eni S.p.A) | Raimondi Cominesi, Nicola (ZFOD) | Guglielmelli, Andrea (ZFOD) | Ali Hassan, Al Attwi Maher (ZFOD) | Ibrahim Uatouf, Kubbah Salma (ZFOD) | Bazzana, Michele (Eni Iraq BV) | Viviani, Enea (Eni Iraq BV)
The Mishrif Formation is one of the important carbonate reservoirs in middle, southern Iraq and throughout the Middle East. In southern Iraq, the formation provides the reservoir in oilfields such as Rumaila/West Qurna, Tuba and Zubair. The top of the Mishrif Formation is marked by a regional unconformity: a long period of emersion in Turonian (ab. 4.4 My) regionally occurred boosted by a warm humid climate, associated to heavy rainfall. In Zubair Field, within the Upper interval of Mishrif Formation, there are numerous evidences of karst features responsible of important permeability enhancements in low porosity intervals that are critical for production optimization and reservoir management purposes.
In the first phase, the integration of Multi-rate Production logging and Well Test analysis was very useful to evaluate the permeability values and to highlight the enhanced permeability (largely higher than expected Matrix permeability) intervals related to karst features; Image log analysis, on the same wells, allowed to find out a relationship between karst features and vug densities, making possible to extend the karst features identification also in wells lacking of well test and Production logging information. This approach has allowed to obtain a Karst/No Karst Supervised dataset for about 60 wells.
In the second phase different seismic and geological attributes have been considered in order to investigate possible correlations with karst features. In fact there are some parameters that show somehow a correlation with Karst and/or NoKarst wells: the Spectral Decomposition (specially 10 and 40 Hz volumes), the detection of sink-holes at top Mishrif on the Continuity Cube and its related distance, the sub-seismic Lineaments (obtained from Curvature analysis and subordinately from Continuity), distance from Top Mishrif. In the light of these results, the most meaningful parameters have been used as input data for a Neural Net Process ("Supervised Neural Network") utilizing the Supervised dataset both as a Trained dataset (70%) and as a Verification dataset (30%). A probability 3D Volume of Karst features was finally obtained; the comparison with verification dataset points out an error range around 0.2 that is to say that the rate of success of the probability Volume is about 80%.
The final outcomes of the workflow are karst probability maps that are extremely useful to guide new wells location and trajectory. Actually, two proof of concept case histories have demonstrated the reliability of this approach. The newly drilled wells, with optimized paths according to these prediction-maps, have intercepted the desired karst intervals as per the subsequent image log interpretation, which results have been very valuable in the proper perforation strategy including low porous intervals but characterized by high vuggy density (Karst features). Based on these promising results the ongoing drilling campaign has been optimized accordingly.
Reservoir structural modelling is one of the fundamental steps in a reservoir study workflow. The impact of the structural uncertainties on the dynamic response of the reservoir is well known and not negligible, but often the reservoir shape is considered as fixed due to the complexity to manage alternative geological structures in multi-realisations simulation loop. Nevertheless, both Risk Analysis (RA) and History Matching (HM) workflows strongly require a practical and time-effective methodology for structure management with an efficient uncertain geometry parameterization. In this work, an innovative methodology for structural uncertainty handling is presented. The methodology is based on the combination of Principal Component Analysis (PCA) and Elastic Gridding. In particular, the PCA-based parameterization is able to efficiently handle the geophysical uncertainty model, consistent with the geostatistical characterization as well. Such methodology has been structured in an internally developed tool. This tool is specifically designed for a direct handling of corner point geometry grids and allows changes of surfaces, shape and size of internal reservoir layers, fault throw and fault position and even new fault placement, honouring geological constraints. One of the key points of the proposed methodology is the integration of a geologically-oriented parameterization and a statistical parameters reduction technique (the above mentioned PCA) in a workflow which includes commercial HM/RA tool and a dynamic simulator. The result is an efficient structural uncertainty management framework suitable for Risk Analysis and History Matching studies. Among the field applications performed so far, two cases have been chosen aiming at showing the potentialities of the proposed approach. The first example is a history matching exercise on an undersatured oil reservoir. A comparison between the traditional and the “structural”, even if simplified, HM is herein provided, showing the improvement due to a better geologically-oriented uncertainty model. The second example is a risk analysis application on an oil field, with a strong uncertainty of the oil in place due to lack of accurate knowledge of the reservoir flanks shape. The application highlights the advantages deriving from the geophysical PCA-based workflow.
The dynamical impact of Structural Uncertainty is well known and not negligible, but often it is not considered in history matching because of its very complex management within the dynamical models.
Indeed, the reservoir geometry is typically kept fixed, and the history matching workflow is usually implemented with a single deterministic reservoir structure. This approach mainly arises from the sequence of operations performed in reservoir structural modelling (interpretation of seismic data, building of the 3D frame, population of the grid with petrophysical properties, upscaling, etc.) which makes very difficult a continuous update of the structural modelling in the framework of an optimization loop. Moreover, the commercial availability of integrated modelling tool is very limited.
The “big loop” approach is a methodology suggested by some authors (see for example Hanea et al., 2013) which aims at building an integrated reservoir modelling chain (from Geophysics and Geology to Reservoir Engineering) that is automated, consistent and updateable. In this way, all the steps of reservoir modelling (from Surface and Fault modelling to Geomodelling, from reservoir simulation to ensemble methods) are unified within a single automatic workflow which requires a strong multidisciplinary collaboration among engineers and geoscientists.
This approach seems to be the way forward, but it involves an all-embracing software chain that is still not available in standard software packages.
To overcome this issue, a more straightforward approach is presented in this paper, based on the combination of elastic gridding and an innovative parameterization of the structural uncertainty.