Liu, Enru (Exxonmobil) | Johns, Mary (Exxonmobil) | Burnett, William (Exxonmobil) | Zhang, Jie (ExxonMobil Upstream Research Co) | Wu, Xianyun (ExxonMobil Upstream Research Co) | Skeith, Gene (Zakum Development Company) | Zelewski, Gregg (ExxonMobil Upstream Research Co)
Summary Fracture characterization based on the concept of seismic anisotropy has become widely used in industry, particularly through the interpretation of azimuthal AVO or AzAVO analyses. However, the integration of seismic anisotropy attributes with other seismic attributes (e.g. In this paper, we present results from a systematic modeling study to address some of these issues. The modeling results presented here have highlighted the complexity of azimuthal anisotropy, and can help to eliminate uncertainties in interpreting seismic anisotropy attributes in terms of natural fractures. The most often used attributes are geometrical attributes (e.g.
Liu, Enru (ExxonMobil Upstream Research Co) | Johns, Mary (ExxonMobil Upstream Research Co) | Zelewski, Gregg (ExxonMobil Upstream Research Co) | Burnett, William A. (ExxonMobil Upstream Research Co) | Wu, Xianyun (ExxonMobil Upstream Research Co) | Zhang, Jie (ExxonMobil Upstream Research Co) | Molyneux, Joe (Formerly ExxonMobil Upstream Research Co. and now in ExxonMobil Canada East) | Skeith, Gene (Zakum Development Co) | Obara, Tomohiro (Zakum Development Co) | El-Awawdeh, Raed (Zakum Development Co) | Sultan, Akmal (Zakum Development Co) | Al Messabi, Abdulrahman (Zakum Development Co)
In this paper, we present a case study of fracture characterization by integrating borehole data with a variety of seismic attributes in a carbonate reservoir from a giant offshore field, United Arab Emirates. The objectives are to determine to what extent seismic data may be confidently used for mapping spatial distributions of subtle faults and fracture corridors in the reservoirs and to better understand the distribution of overburden anomalies (karsts, high impedance channels) for field development planning. Borehole data used in our study include information from core descriptions (fracture density and orientations), image logs, cross-dipole shear-wave anisotropy analysis, and dynamic data (well testing, PLT, tracer, and mud-loss). The seismic attributes include standard and advanced post-stack geometrical attributes; pre-stack seismic azimuthal AVO attributes, and recently developed pre-stack diffraction imaging. We find that there are common features that can be identified in different attributes, and the differences may indicate different scales of fractures. We also observe a qualitative correlation in the area of history match challenges and high anisotropy magnitude, where seismic anisotropy can identify relatively high fracture intensity regions/zones instead of pinpointing individual fractures and complements other attributes as differences do exist between seismically identified fracture zones and well data due to overburden anisotropy, resolution and sampling issues (which are addressed using the synthetic modeling approach). Diffraction attributes have revealed more detailed geological features in overburden (e.g. karsts) and reservoirs (e.g. lineaments) than in reflection data and a comparison with mud loss data in the shallow zones looks promising with a good correlation between mud loss and collapsed features. This work has provided an improved understanding of the applicability of the using multi-seismic attributes for fracture characterizations in carbonate reservoirs.
Faster reservoir simulation turnaround time continues to be a major industry priority. Simultaneously, model sizes are reaching a billion cells and the recovery mechanisms and reservoir management processes to be modeled are rapidly changing and are becoming computationally more expensive. A new reservoir modeling solution has been developed to quickly solve these largest and most complex modeling studies within ExxonMobil.
This latest generation reservoir simulator has been designed from the ground up with 60 years of internal reservoir simulator development experience. Some of the key learnings incorporated into the new reservoir simulator include a requirement for a flexible and modular software framework, a general flow formulation that is decoupled from a very fast and highly accurate phase behavior engine, and unstructured architecture for unstructured grids. This simulator is optimized for massive distributed memory parallelism to take advantage of ExxonMobil's world class supercomputer, Discovery.
A new fluid agnostic formulation has been developed based on general material balance. A highly optimized and highly accurate fluid library supports liquid-liquid-vapor calculations and is the only differentiation between black-oil and compositional options. These considerations are critical for maintaining computationally efficient software under heavy software development with a large development team.
The distributed memory parallelism has been tested and has shown to be very efficient on the Discovery platform. Strong scalability tests have been run to 16,000 cores with good parallel performance to 6000 unknowns per core. These are some of the largest core counts for parallel reservoir simulation with unstructured grids seen in the industry and reduce model run times from days to minutes. These drastically reduced run times have allowed the new simulator to include heavy computational methods, such a nonlinear finite volumes or implicit reactions, for practical use as well as supporting very large models in excess of 100 million cells.
Flexible well management control is provided through Python scripts. This allows users to customize asset-specific control strategies via a well-known and straight-forward scripting language. Alternatively, an internal optimization engine can manage the field subject to high level constraints provided by the user.
Kumar, A. (ExxonMobil Upstream Research Co) | Fairchild, D.P. (ExxonMobil Upstream Research Co) | Macia, M.L. (ExxonMobil Upstream Research Co) | Anderson, T.D. (ExxonMobil Upstream Research Co) | Jin, H.W. (ExxonMobil Research and Engineering Company) | Ayer, R. (ExxonMobil Research and Engineering Company) | Ma, N. (ExxonMobil Research and Engineering Company) | Ozekcin, A. (ExxonMobil Research and Engineering Company) | Mueller, R.R. (ExxonMobil Research and Engineering Company)
As industry continues to explore ever more challenging areas, high end imaging is playing an increasingly important role in lead identification and prospect maturation. We present a case study of a successful multiyear, cross-functional strategy for tackling Anisotropic Pre-Stack Depth Migration (APSDM) on the Gulf of Mexico (GOM) Louisiana Shelf. Understanding of the area-wide anisotropy behavior, creation of a geologic model of anisotropy, and acquisition of borehole data to constrain anisotropy were essential to the success of the APSDM. The early incorporation of all available geophysical data and integration of project geophysicists and geologists was also key. The final seismic volumes displayed excellent imaging and well/seismic agreement without the typical PSDM Z to D artifacts. The volumes affected many leads/prospects, were used to drill near field wildcats and exploration wells, and are still being used by ExxonMobil and partners to pursue additional opportunities on the GOM shelf.
Passey, Q.R. (ExxonMobil Upstream Research Co) | Dahlberg, K.E. (PetroGeeks LLC) | Sullivan, K.B. (McKinsey & Co.) | Yin, H. (ExxonMobil Upstream Research Co) | Xiao, Y.H. (ExxonMobil Exploration Co.) | Guzman-Garcia, A.G. (ExxonMobil Exploration Co.) | Brackett, R.A. (McKinsey & Co.)