Considering carbonate oil reservoirs, a rock fracture is a planar-shaped void filled with oil, water, gas and/or rock fines. These fractures vary in scale forming connected and complex networks of fractures. They have an effect on deliverability of fluids depending on their geometrical complexity, extent, matrix-fracture interaction, wettability, and orientation. In fractured reservoir rocks, relative to the rock matrix, fractures form highly permeable flow pathways that dominate fluid flow and transport in the reservoir which might have favorable or non-favorable effects on hydrocarbon production. It is crucial to characterize the fluid flow in the fracture networks to examine the root-cause relationships, the impact on hydrocarbon recovery and quantify the efficiency of enhanced recovery mechanisms. This work describes the development of a machine learning model for history matching and predicting two-phase relative permeability. Capitalizing on the main principles of the 4th Industrial Revolution (IR 4.0), the development of this model was achieved by training machine learning (ML) algorithms and using advanced predictive data analytics on data collected from lab experiments as input. The model derived from the analysis describes two-phase flow of oil and water in a single discretized fracture taking into account fracture aperture, wall roughness, orientation and, flow rates and direction. It also accommodates fluids and fracture characteristics to match laboratory SCAL experimental of co-current oil and water flow in a mixed-wettability single fracture modeled as narrow gap in a Hele-Shaw cell. The experimental data exhibit variations in shape and end-points that mainly reflect the effects of fracture aperture, roughness, inclination, and hysteresis effects. This in turn demonstrate the effects of phase interference, saturation changes, and major forces acting on two-phase flow in fractures like capillary and viscous forces. The empirical relationship showed an acceptable match to the experimentally derived relative permeability in most of the cases as well as good predictive capabilities against the blind tests on other sets of experimental data and numerical simulation models. Having both fracture relative permeability data (describing the fluids flow) and detailed fracture characterization improves our understanding of the reservoir dynamics and fractured network impact on hydrocarbon recovery.

Reservoir model history matching is a complex, time-consuming, and resource intensive process that needs to be carried out carefully for building reliable predictive tools to manage Oil & Gas assets. Reservoir models encompass detailed geological description representing subsurface heterogeneities that influence its dynamics. To intelligently manage and preserve the complexity of the reservoir models, an artificial intelligence, Progressive-Recursive Self-Organizing Maps (PR-SOM), algorithm was developed. PR-SOM is an unsupervised artificial intelligence neural network algorithm that classifies the reservoir grid cells into progressive reservoir parameters to identify similarly adjoining regions. The algorithm explores and identifies model geo-bodies with similarities and dissimilarities in a progressive and recursive manner. This allows history matching to be conducted on much smaller subsets of the reservoir model of similar geological features. In this work, an artificial intelligence (AI) algorithm was applied, first, to guide the reservoir-wide history match processes. Next, the algorithm was applied to fine-tune well performance using information form well testing and historical data. The algorithm uses both static properties (permeability, rock quality indices, porosity, flow zonation … etc.) and dynamic properties (pressure or saturation) to construct similarities matrix. The results show that the clusters’s growth is progressive, controlled and quality assured by accounting for the controlling reservoir parameters. The number of mapped regions (clusters) is determined by optimizing the similarity matrix recursively. The quality of the global reservoir history match shows the effectiveness of the algorithm, better quality matching for historical production data, and fewer iterations (i.e. less simulation runs). The process is repeated to calibrate the reservoir model near wellbores by limiting the AI algorithm to only the drainage regions seen from well tests and historical data. The results show that employed AI-guided history matching revealed similarities and dissimilarities in the reservoir model. That not only enhance field and well match, but also allowed us to maintain the heterogeneity contrasts inherited from the Earth model. The advanced algorithm was successfully used to assess the extent of geological heterogeneity and its impact on reservoir dynamics, to enhance history match quality, minimize human interaction, and to reduce computational requirements.