In the complete paper, the authors present a novel methodology to model interwell connectivity in mature waterfloods and achieve an improved reservoir-energy distribution and sweep pattern to maximize production performance by adjusting injection and production strategy on the well-control level. A Drilling Advisory System (DAS) is a rig-based drilling-surveillance and -optimization platform that encourages regular drilloff tests, carefully monitors drilling performance, and provides recommendations for controllable drilling parameters to help improve the overall drilling process. This paper proposes a framework based on proxies and rejection sampling (filtering) to perform multiple history-matching runs with a manageable number of reservoir simulations.
A challenging problem of automated history-matching work flows is ensuring that, after applying updates to previous models, the resulting history-matched models remain consistent geologically. To enhance the applicability of localization to various history-matching problems, the authors adopt an adaptive localization scheme that exploits the correlations between model variables and observations. This paper presents a novel approach to generate approximate conditional realizations using the distributed Gauss-Newton (DGN) method together with a multiple local Gaussian approximation technique. This work presents a systematic and rigorous approach of reservoir decomposition combined with the ensemble Kalman smoother to overcome the complexity and computational burden associated with history matching field-scale reservoirs in the Middle East. This paper presents a comparison of existing work flows and introduces a practically driven approach, referred to as “drill and learn,” using elements and concepts from existing work flows to quantify the value of learning (VOL).
This course covers introductory and advanced concepts in streamline simulation and its applications. We will review the theory of streamlines and streamtubes in multi-dimensions. Applications include slow visualization, swept volume calculations, rate allocation and pattern balancing, waterflooding management and optimization, solvent flooding, ranking geostatistical realizations, upscaling/upgridding, history matching and dynamic reservoir characterization. Discussions will include the strengths and limitations of streamline modeling compared with finite difference simulation. PC-Windows based computer programs are used to illustrate the concepts.
Proper characterization of heterogeneous rock properties and hydraulic fracture parameters is essential for optimizing well spacing and reliable estimation of EUR in unconventional reservoirs. High resolution characterization of matrix properties and complex fracture parameters requires efficient history matching of well production and pressure response. We propose a novel reservoir model parameterization method to reduce the number of unknowns, regularize the ill-posed problem and enhance the efficiency of history matching of unconventional reservoirs.
Our proposed method makes a low rank approximation of the spatial distribution of reservoir properties taking into account the varying model resolution of the matrix and hydraulic fractures. Typically, hydraulic fractures are represented with much higher resolution through local grid refinements compared to the matrix properties. In our approach, the spatial property distribution of both for matrix and fractures is represented using a few parameters via a linear transformation with multiresolution basis functions. The parameters in transform domain are then updated during model calibrations, substantially reducing the number of unknowns. The multiresolution basis functions are constructed by eigen-decomposition of an adaptively coarsened grid Laplacian corresponding to the data resolution. High property resolution at the area of interest through the adaptive resolution control while keeping the original grid structure improves quality of history matching, reduces simulation runtime and improves the efficiency of history matching.
We demonstrate the power and efficacy of our method using synthetic and field examples. First, we illustrate the effectiveness of the proposed multiresolution parameterization by comparing it with traditional method. For the field application, an unconventional tight oil reservoir model with a multi-stage hydraulic fractured well is calibrated using bottom-hole pressure and water cut history data. The hydraulic fractures as well as the stimulated reservoir volume (SRV) near the well are represented with higher grid resolution. In addition to matrix and fracture properties, the extent of the SRV and hydraulic fractures are also adjusted through history matching using a Multiobjective Genetic Algorithm. The calibrated ensemble of models are used to obtain bounds of production forecast.
Our proposed method is designed to calibrate reservoir and fracture properties with higher resolution in regions that have improved data resolution and higher sensitivity to the well performance data, for example the SRV region and the hydraulic fractures. This leads to a fast and efficient history matching workflow and enables us to make optimal development/completion plans in a reasonable time frame.
Zhang, Yanbin (Chevron Corporation) | Yang, Changdong (Chevron Corporation) | He, Jincong (Chevron Corporation) | Wang, Zhenzhen (Chevron Corporation) | Xie, Jiang (Chevron Corporation) | Wen, Xian-Huan (Chevron Corporation)
Many shale and tight reservoirs produce significant amount of water. The produced water may come from the injected water pumped during hydraulic fracturing, the formation water, or both. The injected water occupies the fracture network and likely imbibes into the formation close to the fractures. It contributes primarily to early water production during the flowback period. The formation water, when above the critical water saturation, will contribute to most of the long-term water production. Traditional production analysis methods such as DCA and RTA will not be able to characterize the water saturation profile from fracture to formation, nor the interaction between water and hydrocarbon flow in the formation. As a result, water production data is either totally ignored or lumped together with hydrocarbons, leading to inaccurate estimation of reservoir parameters and unreliable forecast of hydrocarbon and water production streams.
Limitations of these traditional production data analysis methods also apply to oil wells that exhibit rapid increases in GOR, as well as gas condensate wells dropping below the dew point. In these cases, the phase behavior of reservoir fluid becomes extremely important, particularly near the hydraulic fractures where the pressure gradient is the highest. 3D full-physics reservoir simulation has been used in the past in these situations, but has faced challenges such as model complexity, water initialization, long simulation time, and difficulty to history match.
We recently proposed a model-based data-driven approach using the Diffusive Diagnostic Function (DDF). In this paper, we will show that the DDF approach can address the above-mentioned difficulties when dealing with multiphase production data in shale and tight reservoirs. It simplifies the 3D problem into 1D simulation models, so that a single forward simulation takes seconds while is still able to capture complex phase behavior and multiphase flow near the fracture and in the formation. It can efficiently and automatically history match multiphase production data using the Ensemble Smoother with Multiple Data Assimilation (ES-MDA) algorithm.
Tripoppoom, Sutthaporn (The University of Texas at Austin / PTT Exploration and Production PLC) | Yu, Wei (The University of Texas at Austin) | Sepehrnoori, Kamy (The University of Texas at Austin) | Miao, Jijun (The University of Texas at Austin / Sim Tech LLC)
Production data which is always available at no additional cost can give an invaluable information of fracture geometry and reservoir properties. However, in unconventional reservoirs, it is insufficient to characterize hydraulic fractures geometry and reservoir properties by only one realization because it cannot capture the non-uniqueness of history matching problem and subsurface uncertainties. Therefore, the objective of this study is to obtain multiple realizations in shale reservoirs by adopting Assisted History Matching (AHM).
We used Neural Network-Markov Chain Monte Carlo (NN-MCMC) algorithm in the proposed AHM workflow for shale reservoirs. The reason is that MCMC, one of AHM in the Bayesian statistics, has benefits of quantifying uncertainty without bias or being trapped in any local minima. Also, using MCMC with neural network (NN) as a proxy model unlocks the limitation of an infeasible number of simulation runs required by a traditional MCMC algorithm. The proposed AHM workflow also utilized the benefits of Embedded Discrete Fracture Model (EDFM) to model fractures with a higher computational efficiency than a traditional local grid refinement (LGR) method and more accuracy than the continuum approach.
We applied the proposed AHM workflow to an actual shale-gas well. We performed history matching on two cases including hydraulic fractures only and hydraulic fractures with natural fractures. The uncertain parameters for history matching consist of fracture geometry, fracture conductivity, matrix permeability, matrix and fracture water saturation, and relative permeability curves. For the case with natural fractures, we included number, length and conductivity of natural fractures as the additional uncertain parameters.
We found that, in this case, the NN-MCMC algorithm find the history match solutions around 30% from a total number of simulation runs. Also, we obtained the posterior distribution of each fracture parameter and reservoir property for both cases. Moreover, we found that the presence of natural fractures affects the posterior distribution. We observed significantly lower fracture height, lower fracture conductivity, higher fracture water saturation than the case without natural fractures because more fluid flow is enhanced by natural fractures. Lastly, the proposed AHM workflow using NN-MCMC algorithm can characterize fracture geometry, reservoir properties, and natural fractures in a probabilistic manner. These multiple realizations can be further used for a probabilistic production forecast, future fracturing design improvement, and infill well placement decision.
It is observed across the global oil and gas industry that all projects are based on specific production forecasts to provide volumes and ensure continuity in the exploration and production business. The majority of these forecasts fall short or under perform based on promises made to the investment community and various stakeholders. There is evidence that industry expectations are usually higher than historical delivery. The onus lies with the exploration, development and production groups tasked with providing this information to find and use all tools and methods available at their disposal and to make realistic corrections during the forecasting process. It is also important to note that a single model will not perfectly represent reality, but we can rely on multiple paths to represent a realistic range of outcomes.
Discounting and risking can help mitigate the effects of our less than perfect knowledge and model shortcomings. Discounting is a pragmatic measure reflecting that it is impossible to capture the full range of all factors contributing to forecast uncertainty. Discount factor must be determined by technical judgment and experience but primarily it must be anchored in historical performance analysis for the field in question, if available, and for analogue fields. That is to say that past under and over performance compared to predictions should guide the discount factor. The latter is preferred as it leads to improved models for future forecasts and better and more consistent discounting with analogue forecasts.
Integration of time-lapse seismic data into dynamic reservoir model is an efficient process in calibrating reservoir parameters update. The choice of the metric which will measure the misfit between observed data and simulated model has a considerable effect on the history matching process, and then on the optimal ensemble model acquired. History matching using 4D seismic and production data simultaneously is still a challenge due to the nature of the two different type of data (time-series and maps or volumes based).
Conventionally, the formulation used for the misfit is least square, which is widely used for production data matching. Distance measurement based objective functions designed for 4D image comparison have been explored in recent years and has been proven to be reliable. This study explores history matching process by introducing a merged objective function, between the production and the 4D seismic data. The proposed approach in this paper is to make comparable this two type of data (well and seismic) in a unique objective function, which will be optimised, avoiding by then the question of weights. An adaptive evolutionary optimisation algorithm has been used for the history matching loop. Local and global reservoir parameters are perturbed in this process, which include porosity, permeability, net-to-gross, and fault transmissibility.
This production and seismic history matching has been applied on a UKCS field, it shows that a acceptalbe production data matching is achieved while honouring saturation information obtained from 4D seismic surveys.