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.
Pore network complexity in carbonate reservoirs is the result of heterogeneous pore size distributions, diagenesis and fractures. Fluid movement through such reservoirs is difficult to model, and permeability depends on the scale considered. Existing permeability computations are empirical in nature, and simply estimate average permeability curves that are hard to upscale. A novel approach for azimuthal and dynamic permeability estimation that preserves formation heterogeneity information is presented through a case study of Jurassic carbonate reservoirs. First, existing petrophysical procedures are extended to take advantage of most Logging-While-Drilling (LWD) data being available in azimuthal fashion as images, to produce azimuthal lithology, porosity and fluid saturation images that retain all the information present in the original LWD images, instead of average results. A new azimuthal permeability image, derived from invasion dynamics, complements the volumetric petrophysical analysis. In general, while drilling, mud filtrate volume is highly correlated to formation permeability, time after bit (TAB), differential pressure, fluid viscosity and mud properties. Therefore, formation permeability can be inverted from the knowledge of all the other parameters. Mud filtrate volume can be computed as an azimuthal image from collocated azimuthal resistivity images at multiple-depths-of-investigation (MDOI), and TAB of such images is readily available as a log. Differential pressure and fluid viscosity can be measured. Finally, mud properties are calibrated to achieve the best match against LWD formation pressure testing stations. A method to compute horizontal (KH) and vertical permeability (KV) from the azimuthal permeability image is also discussed. LWD data from several horizontal wells were processed and the benefits of displaying the resulting volumetric petrophysical analysis images in 3D are discussed. The processed resistivity images confirm heterogeneous /complex formation texture, with thin layering (~ 1 to 3" thickness), different vugs' size and type (within beds, along bedding planes, or along fractures), burrows patterns, and open, closed, and drilling induced fractures. The formation permeability image results match oriented formation tester data with ~ 95% confidence, over the entire range of measured permeability (~ 0.2 mD to 2 D). Image-derived high-resolution KH and KV were computed at different scales, taking into account true bed thickness (TBT) computation. The High-resolution resistivity images were also processed to study fracture distribution, by fracture type, and to extract fracture attributes. KH and KV can be used as direct input for smart completion design with Inflow Control Devices (ICDs), or Interval Control Valves (ICVs). The azimuthal volumetric petrophysical analysis and formation permeability results presented in the case study represent a step improvement in heterogeneous carbonate reservoir characterization, and constitute the first quantitative application of invasion physics while drilling to dynamic permeability estimation.
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.
In this study, core and log data were integrated to identify intervals which are rich in stromatoporoids in an Upper Jurassic carbonate reservoir of an offshore green field Abu Dhabi. The main objective of this study was to recognize and stromatoporoids floatstones/rudstones in core, and develop criteria and workflow to identify them in uncored wells using borehole images. The following workflow was used during this study: i) Identification of the stromatoporoid feature in pilot wells with core and borehole images, ii) Investigate the properties and architecture of stromatoporoid bodies, iii) Integrate the same scale of core observations with borehole images and conventional log data (gamma ray, neutron porosity and bulk density logs) to identify stromatoporoid-rich layers, iv) Performing a blind test on a well by using the criteria developed from previous steps to identify "stromatoporoid accumulations" on a borehole image, and validate it with core observations. In the reservoir under investgation, stromatoporoid floatstones/rudstones intervals were identified and recognized both on core and borehole image in the pilot wells. These distinct reservoir bodies of stromatoporoids commonly occur in upper part of the reservoir and can reach to a thickness of around 20ft. The distribution and thickness of stromatoporoid bodies as well as growth forms (massive versus branching) were recognized on core and borehole images. The accumulations varied between massive beds of containing large pieces of stromatoporoids and grainstone beds rich in stromatoporoid debris. The massive beds of stromatoporoid accumulations are well developed in the northern part of the field. These layers can enhance the reservoir quality because of their distinct vuggy porosity and permeability that can reach up to several hundred of milidarcies (mD). Therefore, it is important to capture stromatoporoid layers both vertically and laterally in the static and dynamic model. Integrating borehole image data with core data and developing a workflow to identify stromatoporoid intervals in uncored wells is crucial to our subsurface understanding and will help to understand reservoir performance. Integration of image log data which is calibrated to core and log data proved to be critical in generating reservoir facies maps and correlations, which were integrated into a sequence stratigraphic framework as well. The results were used in the static model in distribution of high permeability layers related to the distribution of stromatoporoids.
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