31 research outputs found
Integration of Time Lapse Seismic Data Using Onset Time and Analysis of Spatial Resolution
Integration of time-lapse seismic data into the reservoir model offers great potential in understanding reservoir flow patterns as well as reservoir properties. However, it also requires the solution of an inverse problem, which poses challenges in terms of dynamic reservoir modeling and seismic history matching to infer reservoir characterization.
In this dissertation, we first present a method for assessing the inversion results in underdetermined problems, resulting in a multi scale data integration, Then, we introduce a novel history matching approach to integrate frequent seismic surveys (4D) using onset times.
In the first part, an analysis of spatial resolution is incorporated into an efficient history matching approach, in order to indicate the reliability of the estimated solution. By examining the spatial resolution in seismic data integration, as a function of derivation type, we evaluate quantitatively the contribution of pressure and saturation changes on the calibrated permeability field.
Next, we present a novel and efficient approach to integrate frequent time lapse (4D) seismic data into high resolution reservoir models based on seismic onset times. Our approach reduces multiple time-lapse seismic survey data into a single map of onset times, leading to substantial data reduction for history matching while capturing all relevant information regarding fluid flow in the reservoir. We demonstrate the practical feasibility of our proposed approach through the heavy oil reservoir at Pad 31 in the Peace River Field (Alberta, Canada) with daily time lapse seismic surveys recorded by a permanently buried seismic monitoring system.
Finally, we quantitatively investigate the effectiveness of the onset time and the amplitude inversion to solve the inverse problem associated with integrating 4D seismic data into the reservoir model.
The results of the study demonstrate the effectiveness of the onset time approach for integrating a large number of seismic surveys by compressing them into a single map. Also, the onset times appear to be relatively insensitive to the petro elastic model but sensitive to the steam/fluid propagation, making it a robust method for history matching of time lapse surveys
Integration of Time Lapse Seismic Data Using Onset Time and Analysis of Spatial Resolution
Integration of time-lapse seismic data into the reservoir model offers great potential in understanding reservoir flow patterns as well as reservoir properties. However, it also requires the solution of an inverse problem, which poses challenges in terms of dynamic reservoir modeling and seismic history matching to infer reservoir characterization.
In this dissertation, we first present a method for assessing the inversion results in underdetermined problems, resulting in a multi scale data integration, Then, we introduce a novel history matching approach to integrate frequent seismic surveys (4D) using onset times.
In the first part, an analysis of spatial resolution is incorporated into an efficient history matching approach, in order to indicate the reliability of the estimated solution. By examining the spatial resolution in seismic data integration, as a function of derivation type, we evaluate quantitatively the contribution of pressure and saturation changes on the calibrated permeability field.
Next, we present a novel and efficient approach to integrate frequent time lapse (4D) seismic data into high resolution reservoir models based on seismic onset times. Our approach reduces multiple time-lapse seismic survey data into a single map of onset times, leading to substantial data reduction for history matching while capturing all relevant information regarding fluid flow in the reservoir. We demonstrate the practical feasibility of our proposed approach through the heavy oil reservoir at Pad 31 in the Peace River Field (Alberta, Canada) with daily time lapse seismic surveys recorded by a permanently buried seismic monitoring system.
Finally, we quantitatively investigate the effectiveness of the onset time and the amplitude inversion to solve the inverse problem associated with integrating 4D seismic data into the reservoir model.
The results of the study demonstrate the effectiveness of the onset time approach for integrating a large number of seismic surveys by compressing them into a single map. Also, the onset times appear to be relatively insensitive to the petro elastic model but sensitive to the steam/fluid propagation, making it a robust method for history matching of time lapse surveys
A General Spatio-Temporal Clustering-Based Non-local Formulation for Multiscale Modeling of Compartmentalized Reservoirs
Representing the reservoir as a network of discrete compartments with
neighbor and non-neighbor connections is a fast, yet accurate method for
analyzing oil and gas reservoirs. Automatic and rapid detection of coarse-scale
compartments with distinct static and dynamic properties is an integral part of
such high-level reservoir analysis. In this work, we present a hybrid framework
specific to reservoir analysis for an automatic detection of clusters in space
using spatial and temporal field data, coupled with a physics-based multiscale
modeling approach. In this work a novel hybrid approach is presented in which
we couple a physics-based non-local modeling framework with data-driven
clustering techniques to provide a fast and accurate multiscale modeling of
compartmentalized reservoirs. This research also adds to the literature by
presenting a comprehensive work on spatio-temporal clustering for reservoir
studies applications that well considers the clustering complexities, the
intrinsic sparse and noisy nature of the data, and the interpretability of the
outcome.
Keywords: Artificial Intelligence; Machine Learning; Spatio-Temporal
Clustering; Physics-Based Data-Driven Formulation; Multiscale Modelin
Recommended from our members
Streamline-Based Time-Lapse-Seismic-Data Integration Incorporating Pressure and Saturation Effects
We present an efficient history-matching technique that simultaneously integrates 4D repeat seismic surveys with well-production data. This approach is particularly well-suited for the calibration of the reservoir properties of high-resolution geologic models because the seismic data are areally dense but sparse in time, whereas the production data are finely sampled in time but spatially averaged. The joint history matching is performed by use of streamline-based sensitivities derived from either finite-difference or streamline-based flow simulation. For the most part, earlier approaches have focused on the role of saturation changes, but the effects of pressure have largely been ignored. Here, we present a streamline-based semianalytic approach for computing model-parameter sensitivities, accounting for both pressure and saturation effects. The novelty of the method lies in the semianalytic sensitivity computations, making it computationally efficient for high-resolution geologic models. The approach is implemented by use of a finite-difference simulator incorporating the detailed physics. Its efficacy is demonstrated by use of both synthetic and field applications. For both the synthetic and the field cases, the advantages of incorporating the time-lapse variations are clear, seen through the improved estimation of the permeability distribution, the pressure profile, the evolution of the fluid saturation, and the swept volumes