Noufal, Abdelwahab (ADNOC - Upstream) | Obaid, Khalid (ADNOC - Upstream) | Al Blooshi, Abdulla (ADNOC - Upstream) | Nehaid, Hani (ADNOC - Upstream) | Basioni, Mahmoud (ADNOC - Upstream) | Alward, Wassem (Schlumberger) | Uruzula, Jaja (Schlumberger) | Shamsal, Sudipan (Schlumberger) | Dasgupta, Suvodip (Schlumberger) | Raina, Ishan (Schlumberger) | Schlicht, Peter (Schlumberger)
Carbonate reservoirs of the Middle East are known for exhibiting highly heterogenous nature in terms of reservoir properties within microscopic intervals of the reservoir, making it difficult to characterize and predict. An integrated approach involving detailed understanding of the fluids volumes porosity distributions, permeability systems, rock textures, reservoir rock types, and natural fracture distribution at different scales is needed. Accurate characterization for the flow networks, complicated by fracturing and diagenesis is fundamental to achieving realistic prediction, better production performance, and increased recovery. The rock texture in carbonate reservoirs is very unstable and continuously undergoing to multiple stages of dissolution, precipitation, and recrystallization, which obscures any relationships that might have existed between depositional attributes, porosity, and permeability. Fractures make it more complex with their different morphology, often further convoluted by leaching through them. Different measurements are needed to build a realistic model of the petrophysical properties of a carbonate formation. The standard resistivity and porosity measurements are often not sufficient to resolve changes in pore size and texture, so additional measurements are required. Workflows using borehole images can be used to extract information on different textural elements and porosity types. With the newly introduced workflow secondary porosity types are distinguished from matrix porosity and proxies for permeability are calculated.
This workflow integrates borehole images and other petrophysical data in sequential steps and provides important reservoir parameters. With the suggested analytical workflow, it is possible to classify the different types of pore space such as connected to vugs (vug to vug), isolated, connected to fractures, aligned at bed boundaries, or within the rock matrix. The contribution of these different pore types to the total porosity of the formation is quantified in addition to the geometric information of different types of heterogeneities. In addition, the connectedness of the different types of porosity is quantified. The connectedness log describes the quantity of connected spots detected from the electrical borehole image and is used as a predictive measure for identifying zones of higher or lower permeability. During operation it serves as an indicator for determining the perforation intervals, in static reservoir modeling it serves as a permeability driver to improve reservoir mapping. We demonstrate an example where the connectedness successfully predicted productive zones, proven by production logging.