No abstract

No abstract

Evaluating natural fractures in tight carbonate reservoirs during the exploration and early development stages is critical in order to reduce geological uncertainty and determine well trajectory in future horizontal drilling. Challenges are often found in both acquiring the adequate data and assessment of the fractures/sub-seismic faults in the oil based mud borehole environment. This paper summarizes part of the experience learned from the use of an optimal dataset in addition to a workflow on fracture characterization for tight deep carbonate reservoirs in Kuwait. In the process of exploration and development of these particular reservoirs, oil-based mud has been used in the drilling process due to the concerns of wellbore stability. Acoustic images and core was acquired in the early stages of the field development. After the invention of a micro-resistivity imaging tool it was used in combination with the acoustic imaging for integrated and enhanced formation evaluation, which allowed reduced coring for a cost saving. The paper explains the advantages and limitations of each image dataset and describes how the acoustic and microresistivity images are complementary to each other. The paper also presents how different datasets gives partial contribution to the overall geological understanding of the field. More importantly, the case study shows that the combination of both image data sets provides a much better and more complete picture of fractures in the wellbore with limited core calibration. Smaller scale faults, which are usually not detected or poorly imaged on seismic, can be interpreted on images with definition of vertical displacement through the integration of well correlation and cross sections. The output from this study provides an essential database for well completion decision, fracture reservoir modeling, infill drilling plan and future horizontal well placement.

Dedicated exploration efforts targeted on the Jurassic reservoirs in North Kuwait culminated in the discovery of six separate fields, encompassing an area of approximately 1,772 km 2 . These reservoirs are known to hold commercial accumulation of gas, gas condensate and volatile oil and are currently in an early phase of development. The producing Jurassic reservoirs belong to the Marrat, Sargelu and Najmah Formations of Toarcian to Tithonian ages. These reservoirs consist of tight carbonates with several other complex lithologies and are naturally fractured with fractures acting as a driver to production in the Najmah and Sargelu formations and fractures acting as production assist in the Marrat Formation. A 3D geological model was developed for these reservoirs. The static geological model consists of a matrix model capturing the facies, and other matrix reservoir properties like porosity, permeability and water saturation; and the fracture model captures the fracture properties like fracture porosity, permeability and matrix to fracture coupling parameters. The model framework was guided by seismic horizon interpretation from 3D seismic data while the model vertical resolution was optimized to adequately capture the log defined reservoir properties. Electro facies were interpreted at wells with extensive core calibration and were used to develop a 3D facies model capturing the worked out depositional environment. This facies model was used to develop the porosity, permeability and water saturation models using the processed wireline log data, core plug based measurements. The 3D fracture model was developed capturing the areal distribution of fractures using the calibrated 3D seismic volume curvature attribute as soft data and constrained vertically by hard data from image log and cores from wells, thus bringing robustness and reliability for planning the development wells. This 3D geological model was optimally upscaled depending on the aerial well density for use of flow simulation studies.

TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractElectrofacies based on conventional logs are found as strongly correlated to core lithofacies, thin section microfacies, and petrophysical measurements (φ/k and MICP) as long as the carbonate pore system remains simple. Once dual porosity is present, it is found that neural network using conventional logs can not distinguish 2 rock types having the same range of porosity but different porosity-permeability relation. The dual porosity system is illustrated by strong leaching (i.e. dissolution) overprinting the primary interparticle porosity of a grainstone, and responsible for an increase of one order of magnitude in permeability. The dissolution is observed by patchy feature on core. Similarly, this high level of heterogeneity can be observed downhole by borehole imaging tool. The heterogeneous porosity map from the image tool is then converted into a single curve representing the secondary porosity. This secondary porosity log is added to the conventional logs as input of the neural network model. Then, neural network can discriminate between 2 rock types with same range of porosity but different porosity-permeability relation.