The shale gas expansion of the early 2000's brought development focus to liquid rich and tight oil plays as gas prices slumped after 2008. The major challenge was that shale gas understanding and technologies had to be adapted to the needs of tight oil resources. In order to accelerate this process avoiding field experimentation, a structured integrated study approach, the Event Solution, was successfully implemented. The Event Solution is a short, intensively collaborative project that compresses major decision cycles, embraces uncertainty and provides a wider range of alternative solutions. The study addressed the most critical elements of fractured carbonate resources, which included natural fracture modeling, geomechanics, production trend analysis, and reservoir modeling.
The need to develop new tools that allow reservoir engineers to optimize reservoir performance is becoming more demanding by the day. One of the most challenging and influential problems facing reservoir engineers is well placement optimization.
The North Kuwait field (NKF) consists of six fields containing four naturally fractured carbonate formations. The reservoirs are composed of relatively tight limestone and dolomite embedded with anhydrate and shale. The fields are divided into isolated compartments based on fault zones and supported by a combination of different fluid compositions, initial pressures, and estimated free-water levels. Due to natural complexity, tightness, and high drilling costs of wells in the NKF, it is very important to identify the sweet spots and the optimum well locations.
This paper presents two intelligent methods that use dynamic numerical simulation model results and static reservoir properties to identify zones with a high-production potential: reservoir opportunity index (ROI) and simulation opportunity index (SOI). The Petrel* E&P software platform was chosen as the integrated platform to implement the workflow. The fit-for-purpose time dependent 2D maps generated by the Petrel platform facilitated the decision-making process used for locating new wells in the dominant flow system and provided immense support for field-development plans.
The difference between the two methods is insignificant because of reservoir tightness, limited interference, and natural uncertainty on compartmentalization. At this stage, pressure is not a key parameter. As a result, unlike brown fields, less weight was given to simulated pressure, and SOI was used to select the well locations.
The results of this study show that implementing these workflows and obtaining the resulting maps significantly improve the selection process to identify the most productive areas and layers in a field. Also, the optimum numbers of wells using this method obtained in less time and with fewer resources are compared with results using traditional industry approaches.
This paper presents a new methodology to continuously update and improve fracture network models. We begin with a hypothetical model whose fracture network parameters and geological information are known. After generating the "exact?? fracture network with known characteristics, the data were exported to a reservoir simulator and simulations were run over a period of time. Intelligent wells equipped with downhole multiple pressure and flow sensors were placed throughout the reservoir and put on production. These producers were completed in different fracture zones to create a representative pressure and production response.
We then considered a number of wells of which static and dynamic data were used to model well fracture density. When new wells were drilled, historical and new data were used to update the fracture density using Artificial Neural Networks (ANN). More dynamic data will be provided as well as more static data when additional wells are drilled. The accuracy of the prediction model depends significantly on the representation of the available data of the existing fracture network. The importance of conventional data and smart data prediction capability was also investigated. A highly sensitive input data was selected through forward selection scheme to train the ANN. Well geometric locations were included as a new link in the ANN regression process. Once the relationship between fracture network parameters and well performance data was established, the ANN model was used to predict fracture density at newly drilled locations. Finally, an error analysis through correlation coefficient and percentage absolute relative error performance was performed to examine the accuracy of the proposed inverse modeling methodology.
It was shown that fracture dominated production performance data collected from both conventional and smart wells allow automatically updating the fracture network model. The technique proposed helps in generating another -readily available at no cost- data source for fracture characterization to be used as supplementary to limited 1-D data obtained from well logs and cores.