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Permeability is an essential parameter for the reservoir characteristics, which controls the flowing fluids in the reservoir hence the sweep efficiency and the ultimate recovery. The common practice in the petroleum industry is coring a limited number of wells, due to the expensive core recovery process, and measuring the permeability in the recovered cores then extend the concluded correlation to the un-cored wells. However, establishing a reliable permeability predictor is not an easy task in many heterogeneous formations due to the spatial variability of the permeability even at very close distances. Therefore, the conventional linear regression has often failed to address the formation’s heterogeneity, and an unsatisfied correlation coefficient has frequently obtained. Lower Qamchuqa formation, which is highly prolific producing formation in the Middle East, has been used as an example of highly heterogeneous carbonate systems. Well log measurements, which are available for most of the wells, in addition to the core data, were used to capture the high heterogeneity of the depositional environment. Core-log depth calibration was first performed to extract the accurate log measurements that correspond to the actual core data depth. Then, both core and log data were listed in a table for the statistical analysis using the neural network (NN) and multivariate regression approaches. A remarkable improvement in the correlation coefficient was obtained using the NN approach. The utilised training data and further verified by the validation data set have obtained a favourable accuracy compared with conventional linear regression or multivariate regression. The NN permeability predictor has proven its ability to overcome the complexity of the carbonate rock textures and the variety of the diagenesis alteration processes, which make the NN approach a superior method in obtaining an improved permeability predictor. Nevertheless, a regular update to estimate the permeability predictor would be necessary when new data acquired. [ABSTRACT FROM AUTHOR]

Naturally fractured carbonate reservoirs are often described as very heterogeneous systems due to the carbonate depositional environment and to the subsequent diagenesis processes such as mineral precipitation in fractures. Fluid flow behaviour in fractures is highly influenced by the fracture aperture size and its morphology. Mineral precipitation can alter the fracture effectiveness to the fluid flow and cause a partial or entire blockage of fractures. Therefore, accurate characterisation of the fracture morphology can help to enhance the prediction of the fluid flow behaviour in naturally fractured carbonate reservoirs. Mineral precipitation on fracture walls can reduce the fracture aperture significantly. As a result, the fracture permeability affected notably, which reduces the flow potential through fractures as well as changes the flow pattern. The objective of this work is to predict the flow behaviour in fractures under various levels of mineral precipitation to mimic reality. We have approached this objective by using outcrop-based models supported by a set of rock and fluid properties of a nearby fractured formation. Then, the model tested for the gas flow using the derivative plot technique of the synthetic well testing data. The simulation results have shown that mineral cementation can cause a partial blockage in fractures, hence a reduction in their flow capacity, as fractures become a less conductive medium. Nevertheless, the matrix medium can enhance the fluid flow in fractures by providing a bypass path to the fluid to overcome the sealed fractures. In this work, a formula has concluded to estimate the reduction in fracture permeability based on the fraction of the precipitated cement. In the studied formation, the core description has shown that 34% of fractures were blocked, which can lead to a reduction in the permeability by 29–64% and by 37–83% with and without matrix contribution, respectively. Thus, including the fracture morphology in the simulation model enables us to predict the performance of fractured carbonate reservoirs accurately. [ABSTRACT FROM AUTHOR]

Carbonate reservoirs in the Middle East hold a significant proportion of the remaining conventional oil reserves, which most of them were classified as naturally fractured. These reservoirs are well known for their low recoveries compared with their counterpart clastic unfractured reservoirs. Therefore, boosting their recoveries can assure to sustain the energy supply to the global oil markets. In this work, an example from the Middle East has been used to evaluate the impact of the fracture characteristics on the reservoir performance using four decades of production history. The characteristics of natural fractures have a remarkable impact on fluid flow behaviour in fractured reservoirs. Besides, these characteristics, such as fracture intensity, determines the matrix block dimensions, which in turn, influence the matrix recovery. However, it is challenging to estimate the fracture characteristics accurately due to the scarcity of the fracture data from subsurface measurements. Therefore, outcrop analogue data can be utilised to enhance the fracture modelling in fractured reservoirs and to fill the data gap between the seismic scale and well scale. In this work, we have integrated the outcrop fracture data with the core observations to improve the fracture modelling in the studied reservoir. Furthermore, a sensitivity study was carried out to investigate the impact of flow curves, gravity drainage mechanism, and its parameters, as well as the fracture storativity on the history matching in fractured reservoirs. Excellent matching results were obtained for the majority of the wells, as indicated by the low mismatch values. The obtained results highlighted the importance of fractures on the fluid flow behaviour, and hence the reservoir performance. Therefore, fracture properties should not be abusively modified using multipliers and local adjustment to avoid nullifying the reservoir characterisation efforts and degrade the simulation model capability for a future prediction. [ABSTRACT FROM AUTHOR]