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Abstract In hydrocarbon reservoirs, reservoir heterogeneity and fluid production/injection result in unique reservoir energy signature (waves/pulses) and determine its shape and propagation. Reservoir engineers uses this propagation of the pressure waves or pules to determine many key reservoir properties (e.g., drainage volumes, reservoir energy, rock properties, decline analysis, etc.) to help in evaluating different field development strategies. The objective of this paper is to illustrate applications of Fast Marching Method (FMM) in assessing reservoir performance, identifying reservoir patterns and anomalies from production/injection data, and predicting the reservoir response when considering modeling uncertainty for model calibration. The proposed hybrid approach in this work is a physics-constrained data-driven approach. It uses the diffusive time-of-flight (DTOF), this represents the propagation time of pressure disturbance/wave from a source or a sink, from which the drainage volumes can be obtained as it is the case in traditional well testing. The DTOF is calculated from the 3D diffusivity equation after the transformation to a 1D equation. The high frequency diffusivity solution can be casted in the form of the Eikonal equation to allow for an analytical computation of the DTOF, which is solved via the FMM. Using the DTOF calculated production and injection rates will help us inferring faults existence and their transmissibility, fracture networks (existence, location, orientation and direction, faultsโ transmissibility, fracturesโ conductivity, and inter-well connectivity network.). The fundamental concept is to formulate a solution of the diffusivity equation that describes the transient flow. In this work, several synthetic models were used to benchmark. The work demonstrates how the DTOF was used to: generate pressure maps for reservoir monitoring, predicts the operational constraints (e.g., bottom-hole pressure) drainage volumes, and predict new wellsโ performance. FMM results approximately matches in terms of well performance compared to simulation results; the DTOF gives a great insight about the pressure drop in the reservoir during the early- and mid-stages of the simulation. For a relatively short time intervals, FMM proved to be computationally efficient with a much shorter turnaround time to solve the problem, and closely matching the results obtained from numerical reservoir simulation. The physics-constrained data-driven using the DTOF was able to identify the pressure drop for the whole reservoir and to predict the bottom-hole pressure for the wells. Using the DTOF, it is possible to infer major geological features such as faults, fracture networks and regional heterogeneity. Fast Marching Method is an efficient method for solving the diffusivity equation for the DTOF to quickly give engineers an insight into the reservoir pressure (energy) and contacted reservoir volumes in order to maintain evergreen reservoir models.
- Asia > Middle East (0.46)
- North America > United States (0.28)
- Geology > Geological Subdiscipline > Geomechanics (0.35)
- Geology > Geological Subdiscipline > Economic Geology > Petroleum Geology (0.34)
Akhil Datta-Gupta, SPE, is regents professor, university distinguished professor, and L.F. Peterson Endowed Chair professor in the Harold Vance Department of Petroleum Engineering at Texas A&M University. His research interests include high-resolution flow simulation, petroleum reservoir management/optimization, large-scale parameter estimation by use of inverse methods, and uncertainty quantification. Datta-Gupta holds a PhD in petroleum engineering from The University of Texas at Austin. He is an SPE Honorary Member and received SPEโs 2003 Lester C. Uren Award and 2009 John Franklin Carll Award. He was elected to the US National Academy of Engineering in 2012. Datta-Gupta may be contacted at datta-gupta@tamu.edu.
- North America > United States > Texas > Travis County > Austin (0.33)
- North America > United States > Texas > Brazos County > College Station (0.33)
Summary Time-lapse seismic analysis has been successfully applied to monitor fluid saturation changes during production. Even though the seismic difference is observable in preferred environments, it might be produced not by changes in fluid saturation but by changes in other reservoir properties such as temperature or pore pressure. This might misdirect the interpretation of time-lapse seismic data. AVO attributes can also be useful tools to distinguish causes of some of these changes in reservoir properties. In this paper we investigate this problem by simulating time-lapse AVO attributes in two different reservoir models. Our model examples show that amplitude changes for far offset data in a light oil saturated reservoir subjected to water injection are twice as large as analogous changes in zero offset data.
An Efficient Deep Learning-Based Workflow for CO2 Plume Imaging Using Distributed Pressure and Temperature Measurements
Nagao, Masahiro (Texas A&M University) | Yao, Changqing (Texas A&M University) | Onishi, Tsubasa (Chevron Corporation) | Chen, Hongquan (Texas A&M University) | Datta-Gupta, Akhil (Texas A&M University)
Abstract Geologic carbon dioxide (CO2) sequestration has received significant attention from the scientific community as a response to global warming due to greenhouse gas emission. Effective monitoring of CO2 plume is critical to CO2 storage safety throughout the life-cycle of a geologic CO2 sequestration project. Although simulation-based techniques such as history matching can be used for predicting the evolution of underground CO2 saturation, the computational cost of the high-fidelity simulations can be prohibitive. Recent development in data-driven models can provide a viable alternative for rapid CO2 plume imaging. Here, we present a novel deep learning-based workflow that can efficiently visualize CO2 plume in near real-time. Our deep learning framework utilizes field measurements, such as downhole pressure, distributed pressure and temperature as input to visualize the subsurface CO2 plume images. However, the high output dimension of CO2 plume images makes the training inefficient. We address this challenge in two ways: first, we output a single CO2 onset time map rather than multiple saturation maps at different times; second, we apply an autoencoder-decoder network to identify lower dimensional latent variables that compress high dimensional output images. The โonset timeโ is the calendar time when the CO2 saturation at a given location exceeds a specified threshold value. In our approach, a deep learning-based regression model is trained to predict latent variables of the autoencoder-decoder network. Subsequently the latent variables are used as inputs of the trained decoder network to generate the 3D onset time image, visualizing the evolving CO2 plume in near real-time. The power and efficacy of our approach are demonstrated using both synthetic and field-scale applications. We first validate the deep learning-based CO2 plume imaging workflow using a 2D synthetic example. Next, the visualization workflow is applied to a 3D field-scale reservoir to demonstrate the robustness and efficiency of the workflow. The monitoring data set consists of distributed temperature sensing (DTS) data acquired at a monitoring well, flowing bottom-hole pressure data at the injection well, and time-lapse pressure measurements at several locations along the monitoring well. Our approach is also extended to efficiently evaluate the uncertainty of predicted CO2 plume images. Additionally, an efficient workflow for optimizing data acquisition and measurement type is demonstrated using our deep learning-based framework. The novelty of this work is the development and applications of a unique and efficient deep learning-based subsurface visualization workflow for the spatial and temporal migration of the CO2 plume. The efficiency and flexibility of the data-driven workflow make our approach suitable for field-scale applications.
- Energy > Oil & Gas > Upstream (1.00)
- Government > Regional Government > North America Government > United States Government (0.46)
Asymptotic methods provide an efficient means by which to infer reservoir flow properties, such as permeability, from timelapse seismic data. A trajectorybased methodology, similar to raybased methods for medical and seismic imaging, is the basis for an iterative inversion of timelapse amplitude changes. In this approach, a single reservoir simulation is required for each iteration of the algorithm. A comparison between purely numerical and the trajectorybased sensitivities demonstrates their accuracy. Analysis of a set of synthetic amplitude changes indicates that we are able to recover largescale reservoir permeability variations from timelapse amplitude data. In an application to actual timelapse amplitude changes from the Bay Marchand field in the Gulf of Mexico, we are able to reduce the misfit by 81 in 12 iterations. The timelapse observations indicate lower permeabilities are required in the central portion of thereservoir.
- North America > United States > Texas (0.28)
- North America > United States > California (0.28)
- North America > United States > Louisiana > Lafourche Parish (0.25)
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock (0.46)
- Geophysics > Time-Lapse Surveying > Time-Lapse Seismic Surveying (1.00)
- Geophysics > Seismic Surveying > Seismic Processing (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling (1.00)
- North America > United States > Louisiana > East Gulf Coast Tertiary Basin > Bay Marchand Field (0.99)
- Europe > United Kingdom > North Sea > Northern North Sea > East Shetland Basin > Block 211/7a > Magnus Field > Kimmeridge Formation > Magnus Formation (0.99)
- Europe > United Kingdom > North Sea > Northern North Sea > East Shetland Basin > Block 211/7a > Magnus Field > Kimmeridge Formation > Lower Kimmeridge Clay Formation (0.99)
- (6 more...)
- Reservoir Description and Dynamics > Reservoir Simulation (1.00)
- Reservoir Description and Dynamics > Reservoir Fluid Dynamics > Flow in porous media (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Production and Well Operations > Well & Reservoir Surveillance and Monitoring (1.00)