In this paper we demonstrate the use of the capillary pressure equilibrium theory (CPET) model to address the effects of partial saturation in order to estimate hydrocarbon saturation in a reservoir volume using acoustic impedances derived by seismic inversion. The data set used here has been donated by BHP Billiton, and is from an offshore oilfield called the Stybarrow field. The set comprises of a well with a 20-foot sandstone oil saturated pay section and 3D pre- and post-stack seismic volumes. Using the provided angle stacks and well log data, a statistical wavelet, and low impedance model, the final impedance model is computed. There are two final impedance models, derived from post-stack, and pre-stack data. The final impedance models are in agreement with one another at each of the well locations, with low impedance at the oil saturated well, and high impedance at the water saturated well. The corresponding CPET model is built based on the empirical porosity from the well log. The rock and fluid properties are available from the logs and petro physical reports provided by BHP Billiton. The CPET model has difficulty distinguishing between 0 and 30% water saturation. The impedances predicted by the CPET model are in good agreement at the two well locations (blind wells), predicting 98% oil saturation in the 97% oil saturated section, and 8% water saturation in the 5% water saturated section of the reservoir. Finally using the CPET workflow, a 3D distribution of saturation was computed from inversion derived acoustic impedance and the CPET model estimated from well log. Unlike conventional approaches of estimating saturation, our method is able to discriminate between patchy and uniform saturation. Our results on Stybarrow field data reveal that the Stybarrow field behaves in a manner very close to the uniform curve at low water saturation. However, starting at 30% water saturation or higher the distribution becomes slightly patchy.
Presentation Date: Wednesday, October 19, 2016
Start Time: 3:35:00 PM
Presentation Type: ORAL
We present a new method for seismic reservoir characterization and reservoir-property modeling on the basis of an integrated analysis of 3D-seismic data and hydraulic flow units, and apply it to an example of a producing reservoir offshore Western Australia. Our method combines hydraulic-unit analysis with a set of techniques for seismic reservoir characterization including rock physics analysis, Bayesian inference, prestack seismic inversion, and geostatistical simulation of reservoir properties. Hydraulic units are geologic layers and zones characterized by similar properties of fluid flow in porous permeable media, and are defined at well locations on the basis of logs, core measurements, and production data. However, the number of wells available for hydraulic- unit analysis is often extremely limited. In comparison, the lateral coverage and resolution of 3D-seismic data are excellent, and can thus be used to extend hydraulic-unit analysis away from well locations into the full 3D reservoir volume. We develop a probabilistic relationship between optimal 3D-seismic-data attributes and the hydraulic units that we determine at well locations. Because porosity and permeability distributions are estimated for each hydraulic flow unit as part of the process, we can use the 3D seismic probabilistic relationships to constrain geostatistical realizations of porosity and permeability in the reservoir, to be consistent with the flow-unit analysis. Reservoir models jointly constrained by both 3D-seismic data and hydraulic flow-unit analysis have the potential to improve the processes of reservoir characterization, fluid-flow performance forecasting, and production data or 4D-seismic history matching.
Forward modelling is a fundamental part of time-lapse seismic feasibility studies and closed-loop seismic reservoir monitoring schemes. The forward modelling workflow represents a complex interaction between multiple domains, each contributing its own set of assumptions, models, and simplifications, based on a priori information. To allow for the best use of the modelled data, the aim ought to be to keep the level of modelling-introduced noise as low as possible to allow for an optimized decision making process on the specifics of a particular time-lapse seismic project. These considerations will have a profound impact on cost and production. It is therefore key to keep 4D signatures subsurface-relevant, avoiding any contributions from low-fidelity forward modelling. To address this challenge, the paper at hand offers a redefined closed-loop seismic reservoir monitoring framework that draws from and caters for a field-scale dynamic integrated Earth model. The validity of the framework and its implications are demonstrated using the purpose built Chimera model which comprises a four-way closure, faulted structure, and represents a turbidite-type reservoir with clastic depositional sequence.
Literature produces many successful examples of timelapse seismic case studies, with carefully analyzed 4D signatures. Calvert (2005) and Johnston (2013) offer detailed insight in this regards, by also simultaneously sketching the state-of-the-art of time-lapse seismic processing. However, quantitative and even qualitative comparisons of differences between predicted and actually measured time-lapse seismic data are much less performed and discussed in open literature, despite the obvious benefit for model reconciliation. An explanation can be found in the assumptions and methodology used for the feasibility study which can be too simplistic to warrant for a meaningful comparison.
When examining 4D signatures, it often turns out that the observed 4D signal is considerably bigger, smaller or different in shape than what was expected from the preceding feasibility study, producing an e term. Given the level of uncertainty within a 4D feasibility study performed, such a mismatch is even expected and the ε term can be further decomposed into three chief elements:
We present a new method and a field data example for creating reservoir models that simultaneously match seismic and geologic data. Our method combines geostatistical simulation and multi-objective optimization, and it is used to improve static reservoir model estimation by simultaneously integrating multiple datasets including well logs, geologic information and various seismic attributes. The main advantage of our approach is that we can define multiple objective functions for a variety of data types and constraints, and simultaneously minimize the data misfits. Using our optimization method, the resulting models converge towards Pareto fronts, which represent the sets of best compromise model solutions for the defined objectives. We test our new method on a producing reservoir offshore Western Australia. The results of our study indicate that improved reservoir models can be obtained using our method, compared to current geostatistical modeling methods.
We present a new method for seismic reservoir characterization and reservoir property modeling based on an integrated analysis of 3D seismic data and hydraulic flow units, and apply it to an example of a producing reservoir offshore Western Australia. Our method combines hydraulic unit analysis with a set of techniques for seismic reservoir characterization including: rock physics analysis, Bayesian inference, pre-stack seismic inversion and geostatistical simulation of reservoir properties. Hydraulic units are geologic layers and zones characterized by similar properties of fluid flow in porous permeable media, and are defined at well locations based on logs, core measurements and production data. However, the number of wells available for hydraulic unit analysis is often extremely limited. In comparison, the lateral coverage and resolution of 3D seismic data is excellent, and can thus be used to extend hydraulic unit analysis away from well locations into the full 3D reservoir volume. We develop a probabilistic relationship between certain 3D seismic data attributes and the hydraulic units we determine at well locations. Since porosity and permeability distributions are estimated for each hydraulic flow unit as part of the process, we can use the 3D seismic probabilistic relationships to constraint geostatistical realizations of porosity and permeability in the reservoir, to be consistent with the flow unit analysis. Reservoir models jointly constrained by both 3D seismic data and hydraulic flow unit analysis can therefore help to improve the accuracy of dynamic reservoir flow simulation and production history matching.
Technology Focus - No abstract available.
The Stybarrow Field is a moderately sized biodegraded 22° API oil accumulation reservoired in Early Cretaceous sandstones of the Macedon Formation in the Exmouth Sub-Basin, offshore Western Australia. The reservoir is comprised of excellent quality, poorly consolidated turbidite sandstones up to 20m thick. The field lies in approximately 800m of water and has been developed with five near-horizontal producers and three water injection wells. The Stybarrow development came online at an initial rate of 80,000BOPD in November 2007.
Due to the lack of significant aquifer support, water injection was planned from start-up for pressure maintenance. Acquisition of a variety of data types have enabled key subsurface challenges to be addressed both before and during production. Structural and stratigraphic complexities influence connectivity and therefore must be fully evaluated in order to achieve optimal sweep. A feasibility study concluded that Stybarrow would be a good candidate for 4D seismic monitoring. Two monitor surveys were acquired and, along with other reservoir surveillance techniques, have been used to refine the geological model.
The first monitor survey at Stybarrow was recorded in November 2008. The results of this survey were in agreement with prior 4D modelling and supported the drilling of a successful development well in the north of the field. A second monitor survey was recorded in May 2011, three and a half years after first oil and at 70% of expected ultimate recovery. This survey is currently being analysed to determine if sweep patterns have changed.
The 4D surveys have proven to be an important tool for understanding subsurface architecture and dynamic fluid-flow behaviour. The results of both 4D seismic surveys have provided significant contributions to understanding the dynamic behaviour within the reservoir to facilitate optimal reservoir management.
Company Profile Series - No abstract available.
Glinsky, Michael E. (BHP Billiton Petroleum, Houston, Texas) | Asher, Bruce (BHP Billiton Petroleum, Houston, Texas) | Hill, Robin (BHP Billiton Petroleum, Perth, Australia) | Flynn, Mark (BHP Billiton Petroleum, Perth, Australia) | Stanley, Mark (BHP Billiton Petroleum, Perth, Australia) | Gunning, James (CSIRO Petroleum, Clayton, Victoria, Australia) | Thompson, Troy (DownUnder GeoSolutions, Perth, Australia) | Kalifa, Jerome (Let It Wave, Ecole Polytechnique, Palaiseau, France) | Mallat, Stephane (Let It Wave, Ecole Polytechnique, Palaiseau, France) | White, Chris (Louisiana State University, Baton Rouge, Louisiana) | Renard, Didier (L'Ecole des Mines, Fontainebleau, France)
Successful appraisal and development of oil and gas fields requires the integration of uncertain subsurface information into multiple reservoir simulation models. This information includes seismic data, various types of well data, and geologic concepts. Over the past five years, a workflow has been developed by various organizations in conjunction with BHP Billiton Petroleum. This distinctive approach focuses first on building mesoscale reservoir models that can be constrained by seismic data (typically with a resolution up to the stratigraphic seismic loop scale, see Prather et al. 2000), then introducing the finer scale geologic concepts and well data needed for reservoir simulation models (stratigraphic 1st and 2nd order subseismic scale, where each order is about a factor of three in size) via a downscaling step that honors mesoscale model constraints. Uncertainty and correlations of the well and seismic measurements are always taken into account. In fact, they are necessary to be able to combine the various measurements. Bayesian probabilistic techniques are used extensively in this process. The result is an ensemble of reservoir simulation models that is consistent with all of the subsurface information.