In a reservoir, faults at the limit of the seismic resolution can be crucial to explain production history and to optimize field development. However, in most cases the detail required to describe such subtle features depends on the assistance of seismic attributes and semi-automated interpretation techniques. We generated a detailed description of the fault network in the South Arne Chalk Group using a workflow based on a globally consistent computation of the seismic dip. This led to more accurate seismic edge attributes than gained with standard dip estimation techniques. We analysed each fault set and qualitatively assessed its control on fluid flow. Our investigation suggests that the two fracture sets that influence production developed along the same WNW-ESE structural trend and cannot be separated based on the seismic data alone. These faults were active both during and post Chalk deposition. We observe ENE -WSW lineaments that match the pattern of a time-lapse seismic amplitude anomaly associated with water injection. It remains to be verified whether these lineaments could be an extension of overburden faults, as well as whether the increased intensity of the fault network as seen on the 2005 v. the 1995 3D seismic survey was caused by production effects.
This Middle East case study addresses the modeling of a green carbonate reservoir which production is controlled by fractures. The workflow integrates 3D seismic, cores, logs and production data to assess and minimize the production forecast uncertainties. The 3D seismic was used both for the characterization of layer-bound fractures, by means of fracture-related attributes, and to locate sub-seismic faults and associated fracture corridors. The extraction of seismic lineaments was performed using edge-enhancing and automated interpretation techniques. A simple approach was introduced to identify the most persistent lineaments and increase the confidence in their structural nature. The distribution and properties of small scale fractures was constrained by a combination of image logs, cores, drilling logs, seismic attributes and petrophysical model. Outside the corridors, the vertical variability of the fracture density was derived from the interpretation of electrical images, whereas its lateral variations could be correlated to petrophysical changes. Two consistent sets of orientations could be identified from image logs, while a detailed comparison between seismic attributes and flowing fractures from well data showed that the most open fractures were located in the vicinity of the faults. Multi-point statistics (MPS) were then used to constrain the location of the most effective fractured zones while retaining spatial consistency and maximum accuracy at the well locations. The final discrete fracture network (DFN) honored all measurements and computed trends and was upscaled to effective fracture properties (porosity, permeability and shape factor) in the reservoir grid. A dual porosity simulation model was generated and the fracture properties were calibrated against dynamic data. The mismatch between observed and simulated dynamic data was ascribed to the uncertainty in the input to the DFN modeling process. To avoid local modifications while preserving the original fracture property distribution and contrast in the model, history matching was performed by iteratively conditioning fracture intensity to dynamic data and subsequently recomputing the effective fracture properties based on the new modified fracture intensity distribution.
A primary objective of the Statfjord 4-D program is to improve recovery by identifying unswept oil pockets in the Brent reservoirs as targets for infill wells. To assist the mapping of bypassed pay from time-lapse seismic data, a detailed 4-D earth model was constructed over the central portion of the Statfjord Field. The earth model integrates seismic data from a pre-production survey acquired in 1979 and repeat surveys from 1991 and 1997. Log data from 180 wells and flow simulation saturation and pressure predictions, at time steps corresponding to the three survey times, have also been incorporated in the earth model. A Statfjord-specific rock model was applied to link the flow simulator response to acoustic impedance and enable the calculation of timedependent synthetics for comparison with the real seismic amplitude data. The rock model predicts significant increase in impedance when water replaces oil in the Brent sands. Seismic difference analysis with region growing techniques reveals the existence of strong production-related anomalies consistent with the rock model predictions. Impedance difference maps were calculated by vertical averaging over the Tarbert Formation (Upper Brent) from inverted volumes for the three surveys. The seismic impedance differences were calibrated against average saturation changes at the well locations, and 91-79 and 97-91 saturation difference maps were calculated via cokriging, using seismic impedance as a guiding attribute. Comparison of the seismic-derived saturation maps with flow simulator predictions reveals several areas that may contain bypassed oil. A 3-D model of 97-91 saturation changes in the Tarbert Formation was also constructed using a Bayesian stochastic simulation technique constrained with seismic impedance difference data. In particular, a probability volume was derived, allowing quantification of uncertainty in the classification of unswept areas. The entire study was conducted using the EarthGM™ software which provides 3-D as well as 4-D earth modelling capabilities.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.