TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractEffective history matching of real fields requires the resolution of two outstanding problems. First, a conflict may exist between the production data and the existing geological model built solely from static information. In resolving this problem one must relate the inherent multi-scale nature of production data to petrophysical properties of the reservoir at the proper scale. Second, during model updates, geological consistency must be maintained by honoring the prior geologic information. The geoscientist has to choose what prior information is well know (hence fixed), and what is uncertain (hence modifiable).In many instances, the type of geostatistical algorithm is fixed, while key prior geostatistical parameters should be perturbed (e.g. facies proportions, petrophysical properties trends, variogram parameters, and random seed).We propose a new methodology that addresses these problems. First, streamlines are used to relate the production data to petrophysical properties at multiple scales. A combination of geostatistical tools (locally varying mean and probability perturbation method) are then used to jointly map multi-scale corrections back to the geological model through changes of the prior geostatistical parameters. The mapping reconciles the fixed prior geologic information with the production data. The geological model is then explicitly recreated by re-running the geostatistical simulation. This approach differs from other history matching techniques where the petrophysical properties of each grid block are modified directly. While a successful history match may be obtained, the resulting model may be inconsistent with important prior information, hence retaining little predictive power.The methodology is demonstrated by applying it to history matching a giant Saudi Arabian carbonate oil reservoir. This reservoir has over 500 producers and over 50 years of historical data with dramatically changing field conditions. In the past, several attempts were made to manually history matching this reservoir. The process was found to be extremely time consuming, involving dramatic local permeability changes which are often not supported by geological data. By applying the new approach, rather than correcting permeability manually, the corrections supplied by the streamlines were used to constrain the geostatistical algorithms, thereby ensuring a consistent geological scenario at every iteration. * 1 c . The new LVM is passed back to the geostatistical algorithm, and the permeability field is rebuilt with a new trend accounting for the water breakthrough data.
The purpose of the simulation history match phase in a study is to achieve a simulation model calibrated to historical performance for predictive production forecasting while preserving reservoir understanding in terms of reservoir characterization and fluid flow mechanisms. The classical history match simulation approach involves running a number of history match simulation cases with modified simulation model variables to obtain only one of the many probable match models to the field data. SPE 120958Starting in the 90s, major oil and gas companies engaged in the concept of uncertainty in field exploration, reservoir characterization 5 and development 4 . The scale, variety and complexity of uncertainty factors from seismic to economics, however, has deterred even the most determined to conclude a practical integrated uncertainty and risk modeling solution 6 . Indeed, most known uncertainty management techniques consider only two possible approaches, namely, a deterministic realization to reflect reservoir physics and flow mechanisms with low and high values for all variables (i.e. uncertainty boundaries). Secondly, a probabilistic technique that encompasses all uncertain factors ranges with lesser detailed attention on reservoir physics and understanding. This paper describes the innovative Saudi Aramco Event Solution process approach to jointly manage and model uncertainty and risk through an integrated static, dynamic and economic field development strategy process. This approach that lies between a conventional deterministic and a fully probabilistic model approach, handles uncertainty through a variable elimination and narrowing down methodology. The Event Solution process involves handling uncertainty through many stages (i.e. static modeling, history match, well design and prediction stages). This paper presents the application of an innovated history match approach that provides all project stakeholders with a shared understanding of critical and non critical uncertainties (static and dynamic) in history match as carried forward to prediction under uncertainty forecasting.
The understanding and accuracy of modeling fluid flow behavior in naturally fractured carbonate reservoir is critical in predicting reservoir sweep efficiency, remaining drilling targets and evaluating field development alternatives. The use of appropriate complex wells design such as Horizontals, (H) Multi-laterals (ML) and completion technology such as equalizers (ICD) or Inflow Control Valves (ICV) are of equal importance. The approach presented in this paper is based on detailed integrated analysis of all available well data including logs, production, pressure transient analysis (PTA), fracture distributions, well flow profiles (PLT) etc, to provide a first-line insight of the fractured reservoir fluid flow mechanism. These first-line insights provide the basis to develop mechanistic or concept reservoir simulation models to fine-tune fluid movement understanding in fractures, reservoir matrix, well types (H, ML) and completion placement to field development strategies. Sector modeling provides further insight to well design in field areas of different rock quality and fracture density. Well type and completion strategy alternatives for each identified field area including intelligent smart well completions are developed and tested in each sector model. The combined developed understanding of fluid flow mechanisms, well type and completion strategy are rolled up and implemented into a full field simulation model, fine tuned through history match and prediction processes. This paper describes the methodology used to study a number of naturally fractured carbonate reservoirs through the integrated "Event Solution"1 study approach. The methodology presented in this paper was applied on a number of large Middle East carbonate fields. The fields studied have naturally fractured reservoirs with two distinct fracture systems. Namely, fracture corridors or clusters and diffuse or layer-bounded fractures. Diffuse fractures are typically horizontal (layer-bounded fractures) inter-connect with the fracture corridors which are normally vertical to sub-vertical. This fracture system combination forms a highly conductive fluid flow and pressure medium which is responsible for observed water movement as well as pressure propagation from the aquifer/injectors into the reservoirs. Background Literature presented some methodologies to numerically simulate fracture corridors and diffuse fractures systems in naturally fractured carbonate reservoirs. Halilu et al.2 presented a method for large fields dominated by clusters of sub-vertical fractures called fracture corridors. In this study, the effective Warren-Root and fracture parameters were adjusted to mimic explicit fracture modeling to represent fractures corridors. The study results showed that fluid flow in these fields is largely influenced by large scale fracture corridors. These large scale fracture corridors were lately named fracture fairways.
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