An ensemble-based history-matching framework is proposed to enhance the characterization of petroleum reservoirs through the assimilation of crosswell electromagnetic (EM) data. As one of advanced technologies in reservoir surveillance, crosswell EM tomography can provide a cross-sectional conductivity map and hence saturation profile at an interwell scale by exploiting the sharp contrast in conductivity between hydrocarbons and saline water. Incorporating this new information into reservoir simulation in combination with other available observations is therefore expected to enhance the forecasting capability of reservoir models and to lead to better quantification of uncertainty.
The proposed approach applies ensemble-based data-assimilation methods to build a robust and flexible framework under which various sources of available measurements can be readily integrated. Because the assimilation of crosswell EM data can be implemented in different ways (e.g., components of EM fields or inverted conductivity), a comparative study is conducted. The first approach integrates crosswell EM data in its original form which entails establishing a forward model simulating observed EM responses. In this work, the forward model is based on Archie's law that provides a link between fluid properties and formation conductivity, and Maxwell’s equations that describe how EM fields behave given the spatial distribution of conductivity. Alternatively, formation conductivity can be used for history matching, which is obtained from the original EM data through inversion using an adjoint gradient-based optimization method. Because the inverted conductivity is usually of high dimension and very noisy, an image-oriented distance parameterization utilizing fluid front information is applied aiming to assimilate the conductivity field efficiently and robustly. Numerical experiments for different test cases with increasing complexity are carried out to examine the performance of the proposed integration schemes and potential of crosswell EM data for improving the estimation of relevant model parameters. The results demonstrate the efficiency of the developed history-matching workflow and added value of crosswell EM data in enhancing the characterization of reservoir models and reliability of model forecasts.
History matching (HM) is a complex process that aims to increase the reliability of reservoir simulation models. HM is an inverse problem with multiple solutions that calls for a probabilistic approach. When observed data are integrated with sampling methods, uncertainty can be reduced by updating the probability density function (
It is well-known that oil- and gasfield development is a high-risk venture. Uncertainties originating from geological models (e.g., structure, stratigraphy, channels, and geobodies) are coupled with uncertainties of reservoir models (e.g., distribution of permeability and porosity in the reservoir) and uncertainties of economic parameters (e.g., oil and gas prices and costs associated with drilling and other operations). It is critically important to properly quantify the uncertainty of such parameters and their effect on production forecasts and economic evaluations. Recently, multiobjective-optimization techniques have been developed to maximize expectations of some economic indicators (e.g., net present value) and, at the same time, to minimize associated uncertainty or risk. Because of limited access to the subsurface reservoir (e.g., it is impossible to measure the permeability and porosity at the location of each gridblock of a simulation model), reservoir properties have quite large uncertainties.
This article, written by Special Publications Editor Adam Wilson, contains highlights of paper SPE 181611, “Uncertainty Quantification for History-Matching Problems With Multiple Best Matches Using a Distributed Gauss-Newton Method,” by Guohua Gao, SPE, Jeroen C. Vink, SPE, Chaohui Chen, SPE, Mohammadali Tarrahi, SPE, and Yaakoub El Khamra, Shell, prepared for the 2016 SPE Annual Technical Conference and Exhibition, Dubai, 26–28 September. The paper has not been peer reviewed.
Dynamic reservoir simulation models have become an essential tool in oil field management. They can be used to explore the impact of alternative development and operating strategies, and can even be used to search for strategies that are optimal in some desired sense. Robust strategies can be obtained by use of an ensemble of models that are all consistent with available data but that adequately represent the uncertainty. In this paper we demonstrate an application of a workflow to obtain multiple models that are all consistent with historic time-lapse seismic data. We show how these models can subsequently be used to obtain optimal recovery strategies, and discuss how this approach can be extended to optimization under uncertainty.
The history matching step is performed using an ensemble-based methodology and an efficient parameterization of the time-lapse seismic data. The optimization step takes the history matched model (or model ensemble to account for all uncertainty) as input. An approximate gradient computation provides improved strategies. The history matching workflow is demonstrated on a representative sector of a field that is developed with both vertical and horizontal wells and is produced with simultaneous injection of water and CO2.
Evaluation of the history matching step indicates a good match with the seismic data, indicating the power of the parameterization method for handling very large numbers of seismic data. It is discussed that production data can be matched simultaneously, given proper characterization of measurement errors. The optimization step is performed assuming an oil recovery scenario based on alternating water and CO2 injection. Appropriate cost models may be employed to arrive at operating scenarios that result in the maximum expected economic value. Results will depend on, amongst other aspects, the chosen economic model, indicating that best operating practices will be region specific.
The combination of advanced yet practical workflows for assisted history matching and recovery optimization is the result of many developments over the past 10 years. We argue that the cumulative result of these efforts provides significant value to field developments by enabling consistency with measurements, reduction of uncertainties and improved optimization of operational strategies.
The oil & gas industry has been the backbone of the world's economy in the last century and will continue to be in the decades to come. With increasing demand and conventional reservoirs depleting, new oil industry projects have become more complex and expensive, operating in areas that were previously considered impossible and uneconomical. Therefore, good reservoir management is key for the economical success of complex projects requiring the incorporation of reliable uncertainty estimates for reliable production forecasts and optimizing reservoir exploitation. Reservoir history matching has played here a key role incorporating production, seismic, electromagnetic and logging data for forecasting the development of reservoirs and its depletion. With the advances in the last decade, electromagnetic techniques, such as crosswell electromagnetic tomography, have enabled engineers to more precisely map the reservoirs and understand their evolution. Incorporating the large amount of data efficiently and reducing uncertainty in the forecasts has been one of the key challenges for reservoir management. Computing the conductivity distribution for the field for adjusting parameters in the forecasting process via solving the inverse problem has been a challenge, due to the strong ill-posedness of the inversion problem and the extensive manual calibration required, making it impossible to be included into an efficient reservoir history matching forecasting algorithm. In the presented research, we have developed a novel Finite Difference Time Domain (FDTD) based method for incorporating electromagnetic data directly into the reservoir simulator. Based on an extended Archie relationship, EM simulations are performed for both forecasted and Porosity-Saturation retrieved conductivity parameters being incorporated directly into an update step for the reservoir parameters. This novel direct update method has significant advantages such as that it overcomes the expensive and ill-conditioned inversion process, applicable to arbitrary reservoir geometries and enables efficient integration with real field crosswell EM data.
The oil and gas industry has a long history of model construction relying on the integration on multiple disciplines including seismic, sedimentology, geology and petrophysics. It is undeniable that all steps are required to lead to a good characterization of the studied reservoir and to provide reliable and realistic predictions. However the necessary time to finalize each step often leads to a modeling process of several years. This paper therefore discusses an alternative workflow, or Fast-Track modeling approach, which allows generating a representative and matched reservoir model in a considerably reduced time. This approach consists in:
This workflow ensures achieving a better petrophysical properties consistency between logs, cores and models, a more representative asset volume estimates and also prevents utilization of non-measured parameters such as irreducible water saturation (Swc's) or unproven and extensive permeability multipliers. This altogether contributes in reducing convergences problems as well as providing more consistent dynamic model setup.
This workflow was applied on a complex offshore reservoir consisting of a large gas cap and significant oil rim. The results and achievements are presented within this paper and demonstrate the suitability of this workflow as a short term alternative and shortcut to complex simulation modeling, in waiting of all adequate studies to be completed and integrated in a detailed reservoir model.
Lun, Lisa (ExxonMobil Upstream Research Co.) | Dunn, Paul Alexander (ExxonMobil Upstream Research Co.) | Stern, David (ExxonMobil Upstream Research Co.) | Oyerinde, Adedayo Stephen (Exxon Mobil Corporation) | Chorneyko, David M. (ExxonMobil Upstream Research Co.) | Stewart, Jonathan (ExxonMobil Exploration and Production Norway) | Fowler, Kenneth (RWTH Aachen) | Nollet, Sofie
Copyright 2012, Society of Petroleum Engineers This paper was prepared for presentation at the SPE Annual Technical Conference and Exhibition held in San Antonio, Texas, USA, 8-10 October 2012. This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright. Abstract This paper describes a history match study carried out on a deep water reservoir with roughly a year of production history, consisting of flowing bottom hole pressure (FBHP) and oil rate measurements. This study demonstrates a procedure that can be used to integrate geologic concepts with the history match process to find a range of geologically realistic reservoir descriptions that are all consistent with production history to date for evaluating uncertainty in predictions. The process has three steps: 1) determine, based on geologic and engineering data, what model inputs will be tested in history matching; 2) develop an experimental design(s) and use it(them) to test the impact of those inputs on reservoir performance; and 3) select models for further history match work or prediction of future performance.
Sena, Armando (The University of Texas) | Sen, Mrinal (The University of Texas) | Stoffa, Paul (The University of Texas) | Seif, Roustam (The University of Texas) | Jin, Long (The University of Texas)