Reservoir Rock Typing and saturation modeling need a two-sided methodology. One side is the geological side of the rock types to populate properties within geological concepts. The other side is addressing reservoir flow and dynamic initialization with capillary pressure. The difficulty is to comply with both aspects especially in carbonates reservoirs with complex diagenesis and migration history. The objective of this paper is to describe the methodology and the results obtained in a complex carbonate reservoir. The approach is initiated from the sedimentological description from cores and complemented with microfacies from thin sections. The core-based rock types use the dominant rock fabrics, as well as the cementation and dissolution diagenetic processes. The groups are limited to similar pore throat size distribution and porosity-permeability relationships to stay compatible with property modeling at a later stage. At log-scale, the rock typing has a focus on the estimation of permeability using the most appropriate logs available in all wells. Those logs are porosity, mineral volumes, normalized saturation in invaded zone (Sxo), macro-porosity from borehole image or Nuclear Magnetic Resonance (NMR), NMR T2 log mean relaxation, and rigidity from sonic logs. A specific calculation to identify the presence of tar is also included to assess the permeability better and further interpret the saturation history. The MICP data defined the saturation height functions, according to the modality of the pore throat size. The log derived saturation, and the SHFs are used to identify Free Water Level (FWL) positions and interpret the migration history. The rock typing classification is well connected with the geological aspects of the reservoirs since it originates from the sedimentological description and the diagenetic processes. We identified a total of 21 rock types across all the formations of interest. We associated rock types with depositional environments ranging from supra-tidal to open marine that controls both the original rock fabrics and the diagenetic processes. The rock typing classification is also appropriate to model permeability and saturation since core petrophysical measurements were in use during the classification. The permeability estimation from logs uses multivariate regressions that have proven to be sensitive to permeability after a Principal Component Analysis per zones and per lithologies. The difference between the core permeability and the permeability derived from logs stays within one-fold of standard deviation as compared to the initial 3-fold range of porosity-permeability. We assigned the rock types with three Saturation Height Function (SHF) classes; (unimodal-dolomite, unimodal- limestone & Multimodal-Limestone). The log derived water saturation (Sw) from logs and SHF shows acceptable agreement. The reservoir rock typing and saturation modeling methodology described in this paper are considerate of honoring geological features and petrophysical properties to solve for complex diagenesis and post-migration fluid alteration and movement processes.
High Permeability layers are a common feature of most carbonate series, and contribute to heterogeneous sweeps and early breakthroughs when injecting gas or water in an oil reservoir. The innovative workflow proposed to model the high permeability layers, on the basis of strong constraints from geological data and concepts, enables to be more predictive on the dynamic behaviour of the reservoir. It also enables a better selection of the most adapted recovery mechanism and a better strategy for optimizing the positioning of future perforations. Increasing efforts are made to track and represent high permeability layers, also named streaks, in 3D dynamic models. Well data (core description, porosity-permeability plug measurements and well tests) allow the recognition of such heterogeneity at the well. Attributing the high permeability layers to fractures or to matrix heterogeneity can be achieved by cross-checking the well data with production tests and sometimes with 3D seismic. The 3D representation of these layers, however, does not necessarily incorporate all the geological understanding that has been gathered. In the case of the studied field, offshore Abu Dhabi, the heterogeneity that is related to high permeability layers is linked to dissolution diagenesis. Dissolution diagenesis occurs preferentially just below the sequence boundaries in carbonate series. Although there are uncertainties on the dissolution trends, a stochastic modeling approach based on geological observations and concepts can help capture the effects of diagenesis on the rock properties. Multiple realizations present various equi-probable images of the field heterogeneity and will guide the field development strategy. In the first stage of the workflow, depositional environments and lithofacies have been modeled in 3D. They are constrained by the well data and the geological concept related to slope profile and to paleobathymetric control over depositional environments. Diagenesis overprints the rock texture by cementing or dissolving. Vugs are typically the product of dissolution diagenesis. No abundant dolomitization is observed in the studied formation. Dissolution diagenesis is one of the major causes of permeability enhancements in carbonates. Dissolution diagenesis preferentially occurs in the shallowest depositional environments, just below sequence boundaries when relative sea level is lowest. It is related to the infiltration of meteoric water in the carbonate series. There are three possible controls over the pathway which meteoric water can follow: the slope, the "weakest" lithofacies and early fissures or fractures in the rocks. The approach proposed to represent the effects of diagenesis is explicit. It is not currently driven by any fracture orientation. Pathways where alteration occurs are stochastically modeled on the basis of well observations and hypotheses on their orientation. Inside these modeled pathways, specific permeability ranges are entered to represent the rock properties. An interesting aspect of using an explicit approach is the quest for predictiveness of the reservoir behaviour. Most approaches related to high permeability streaks modeling today are implicit: high permeabilities observed at the wells are extrapolated away from the well by kriging or stochastic simulation methods without much geological constraints. In some cases a very fine vertical layering is chosen in order to capture even the slightest permeability variations vertically along the well.
This study demonstrates first time in Abu Dhabi the capacity of combined 3D tar and petroleum systems modelling, in an operating oil field offshore Abu Dhabi, indicating locally the tar presence in the Arab formation. It is intended to give insights on the most likely process of tar formation and allows to a certain extent a prediction of tar presence away from well control. The tar modelling is calibrated through core observations on vertical wells, which define the thickness of the initially assumed constant and homogeneous tar mat. An extensive data set using adapted geochemistry, petrographic analyses, fluid inclusion analyses and inclusion PVTX-modelling is used to analyse the charge history of the oil field and its tar in detail. The analysed tar occludes the pore space in the reservoirs of the lower Arab Formation in the oilfield offshore Abu Dhabi. The petrographic analyses indicate the presence of tar particles even up to the upper Arab. Geochemistry and petrography show that there are two different tar types. The classical reservoir filling black tar in the upper most part of the Lower Arab is identified as APE (asphaltene precursor entity after Wilhelms & Larter, 1994) tar which is caused by a flocculation process at a certain temperature and pressure regime in the reservoir. This concept has been successful modelled and can even explain the observed fine tar particles up to the upper most Arab. The second type of initially called "tar" is analysed and observed in the top Diyab and lowest part of the Arab, in a micritic limestone facies environment. Previous concepts struggled to justify the black tar deposition in the dense micritic carbonate mudstones. The initial porosity in this micritic mudstones was already very low and therefore a tar flocculation process or gravity segregation in such an environment urges for other explanations. Our analyses indicate that the micritic mudstone acts as a source rock at the top of Diyab and the lower most Arab subunit, where the early heavy oil and asphaltenes (POA=pre-oil asphaltenes) did not leave the rock and stayed in-situ as bitumen/black tar. This has been modelled with a tar specific kinetics, differentiating in an early heavy oil component (POA), that is generated in-situ and an asphaltene component (APE) expelled within the oil and transported into the reservoir. Acceptable tar modelling result have been reached by reconstructing the charge history of the field. It shows that Diyab oil entered the lower Arab reservoir at approx. 105/95 Ma. The tar modelling through time shows that first tar deposited at 78 Ma (+/- 5 Ma) in the southern part of the oil field. The charge modelling indicates the lower Arab seal failure at approx. 58/53 Ma in the past. The shallower reservoir units of the lower and middle Arab up to the upper Arab are subsequently filled with asphaltene rich oil. Then at 48 Ma the asphaltenes reach a flocculation peak. Finally at 47/34 Ma the whole oil field with the already flocculated tar (APE in the reservoir) and the asphaltenes in the source rock (POA) received a paleo heat shock of at least 140°C, which transformed the tar into pyrobitumen and caused the today surprisingly high API (around 40°API) in the oil field by oil-to-gas cracking.
This paper describes the use of Artificial Intelligence (AI) to support well planning in an Abu Dhabi offshore field. In this application, AI has been used for automated and unbiased evaluation of well trajectories, with the objective to optimize the cost, risk versus value trade-offs while considering complex issues such as anti-collision with existing wells. A Rapid Random Tree (RRT) algorithm, well known for applications in robotics, has been used to generate well trajectories for 2 actual drilling projects. The algorithm creates a full and unbiased option space of feasible well trajectories, presented in a custom-built and collaborative digital solution. Results demonstrate that AI-generated well trajectories were 2-5% shorter than manually planned and/or actual drilled wells. This use case also shows that an AI can design thousands of possible well trajectories in only a few hours, adhering to well design rules and anti-collision constraints. This would lead to significant time savings, and possibly material drilling cost reductions, in even more congested brownfield assets. This paper describes a real application of AI-assisted well trajectory planning in an operational setting, with a comparison to manually planned and actual drilled wells. As such, this provides a rather unique insight into the business value-adding potential of Artificial Intelligence in traditionally manual work processes.
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