History Matching and Performance Prediction of a Polymer Flood Pilot in Heavy Oil Reservoir on Alaska North Slope

2021 ◽  
Author(s):  
Xindan Wang ◽  
Cody Keith ◽  
Yin Zhang ◽  
Abhijit Dandekar ◽  
Samson Ning ◽  
...  
Keyword(s):  
Simulation Model ◽  
Oil Recovery ◽  
Reservoir Simulation ◽  
Heavy Oil ◽  
History Matching ◽  
Polymer Flooding ◽  
North Slope ◽  
Polymer Flood ◽  
Reservoir Simulation Model ◽  
Water Cut

Abstract The first-ever polymer flood pilot to enhance heavy oil recovery on Alaska North Slope (ANS) is ongoing. After more than 2.5 years of polymer injection, significant benefit has been observed from the decrease in water cut from 65% to less than 15% in the project producers. The primary objective of this study is to develop a robust history-matched reservoir simulation model capable of predicting future polymer flood performance. In this work, the reservoir simulation model has been developed based on the geological model and available reservoir and fluid data. In particular, four high transmissibility strips were introduced to connect the injector-producer well pairs, simulating short-circuiting flow behavior that can be explained by viscous fingering and reproducing the water cut history. The strip transmissibilities were manually tuned to improve the history matching results during the waterflooding and polymer flooding periods, respectively. It has been found that higher strip transmissibilities match the sharp water cut increase very well in the waterflooding period. Then the strip transmissibilities need to be reduced with time to match the significant water cut reduction. The viscous fingering effect in the reservoir during waterflooding and the restoration of injection conformance during polymer flooding have been effectively represented. Based on the validated simulation model, numerical simulation tests have been conducted to investigate the oil recovery performance under different development strategies, with consideration for sensitivity to polymer parameter uncertainties. The oil recovery factor with polymer flooding can reach about 39% in 30 years, twice as much as forecasted with continued waterflooding. Besides, the updated reservoir model has been successfully employed to forecast polymer utilization, a valuable parameter to evaluate the pilot test’s economic efficiency. All the investigated development strategies indicate polymer utilization lower than 3.5 lbs/bbl in 30 years, which is economically attractive.

History to Prediction Transition Calibration of Reservoir Simulation Model Ensemble

2021 ◽  
Author(s):  
Bjørn Egil Ludvigsen ◽  
Mohan Sharma
Keyword(s):  
Simulation Model ◽  
Reservoir Simulation ◽  
History Matching ◽  
Simulation Models ◽  
Productivity Index ◽  
Model Parameters ◽  
Gas Oil ◽  
Well Performance ◽  
Reservoir Simulation Model ◽  
Water Cut

Abstract Well performance calibration after history matching a reservoir simulation model ensures that the wells give realistic rates during the prediction phase. The calibration involves adjusting well model parameters to match observed production rates at specified backpressure(s). This process is usually very time consuming such that the traditional approaches using one reservoir model with hundreds of high productivity wells would take months to calibrate. The application of uncertainty-centric workflows for reservoir modeling and history matching results in many acceptable matches for phase rates and flowing bottom-hole pressure (BHP). This makes well calibration even more challenging for an ensemble of large number of simulation models, as the existing approaches are not scalable. It is known that Productivity Index (PI) integrates reservoir and well performance where most of the pressure drop happens in one to two grid blocks around well depending upon the model resolution. A workflow has been setup to fix transition by calibrating PI for each well in a history matched simulation model. Simulation PI can be modified by changing permeability-thickness (Kh), skin, or by applying PI multiplier as a correction. For a history matched ensemble with a range in water-cut and gas-oil ratio, the proposed workflow involves running flowing gradient calculations for a well corresponding to observed THP and simulated rates for different phases to calculate target BHP. A PI Multiplier is then calculated for that well and model that would shift simulation BHP to target BHP as local update to reduce the extent of jump. An ensemble of history matched models with a range in water-cut and gas-oil ratio have a variation in required BHPs unique to each case. With the well calibration performed correctly, the jump observed in rates while switching from history to prediction can be eliminated or significantly reduced. The prediction thus results in reliable rates if wells are run on pressure control and reliable plateau if the wells are run on group control. This reduces the risk of under/over-predicting ultimate hydrocarbon recovery from field and the project's cashflow. Also, this allows running sensitivities to backpressure, tubing design, and other equipment constraints to optimize reservoir performance and facilities design. The proposed workflow, which dynamically couple reservoir simulation and well performance modeling, takes a few seconds to run for a well, making it fit-for-purpose for a large ensemble of simulation models with a large number of wells.

scholarly journals Combination of Cyclic Steam Stimulation and Steam Flooding to Improve Oil Recovery in Unconsolidated Sand Heavy Oil Reservoir

2020 ◽  
Vol 9 (2) ◽  
pp. 80-87
Author(s):  
Ahmad Muraji Suranto ◽  
Boni Swadesi ◽  
Indah Widyaningsih ◽  
Ratna Widyaningsih ◽  
Sri Wahyu Murni ◽  
...  
Keyword(s):  
Oil Recovery ◽  
Reservoir Simulation ◽  
Heavy Oil ◽  
Steam Injection ◽  
Steam Chamber ◽  
Steam Flooding ◽  
Cyclic Steam Stimulation ◽  
Unconsolidated Sand ◽  
Reservoir Simulation Model ◽  
Steam Stimulation

Steam injection can be success in increasing oil recovery by determining the steam chamber growth. It will impact on the steam distribution and steam performance in covering hot areas in the reservoir.  An injection plan and a proper cyclic steam stimulation (CSS) schedule are critical in predicting how steam chamber can grow and cover the heat area. A reservoir simulation model will be used to understand how CSS really impact in steam chamber generation and affect the oil recovery. This paper generates numerous scenarios to see how steam working in heavy oil system particularly in unconsolidated sand reservoir. Combine the CSS method and steam injection continue investigate in this research. We will validate the scenarios based on the how fast steam chest can grow and get maximum oil recovery. Reservoir simulation resulted how steam chest behavior in unconsolidated sand to improve oil recovery; It concluded that by combining CSS and Steam Injection, we may get a faster steam chest growth and higher oil recovery by 61.5% of heavy oil system.

Constructing Dual-Porosity Models from High-Resolution Discrete-Fracture Models Using Deep Neural Networks

2021 ◽  
Author(s):  
Xupeng He ◽  
Ryan Santoso ◽  
Marwa Alsinan ◽  
Hyung Kwak ◽  
Hussein Hoteit
Keyword(s):  
Simulation Model ◽  
Oil Recovery ◽  
Reservoir Simulation ◽  
Water Saturation ◽  
Dual Porosity ◽  
Discrete Fracture Model ◽  
Discrete Fracture ◽  
Dual Porosity Model ◽  
Reservoir Simulation Model ◽  
Porosity Model

Abstract Detailed geological description of fractured reservoirs is typically characterized by the discrete-fracture model (DFM), in which the rock matrix and fractures are explicitly represented in the form of unstructured grids. Its high computation cost makes it infeasible for field-scale applications. Traditional flow-based and static-based methods used to upscale detailed geological DFM to reservoir simulation model suffer from, to some extent, high computation cost and low accuracy, respectively. In this paper, we present a novel deep learning-based upscaling method as an alternative to traditional methods. This work aims to build an image-to-value model based on convolutional neural network to model the nonlinear mapping between the high-resolution image of detailed DFM as input and the upscaled reservoir simulation model as output. The reservoir simulation model (herein refers to the dual-porosity model) includes the predicted fracture-fracture transmissibility linking two adjacent grid blocks and fracture-matrix transmissibility within each coarse block. The proposed upscaling workflow comprises the train-validation samples generation, convolutional neural network training-validating process, and model evaluation. We apply a two-point flux approximation (TPFA) scheme based on embedded discrete-fracture model (EDFM) to generate the datasets. We perform trial-error analysis on the coupling training-validating process to update the ratio of train-validation samples, optimize the learning rate and the network architecture. This process is applied until the trained model obtains an accuracy above 90 % for both train-validation samples. We then demonstrate its performance with the two-phase reference solutions obtained from the fine model in terms of water saturation profile and oil recovery versus PVI. Results show that the DL-based approach provides a good match with the reference solutions for both water saturation distribution and oil recovery curve. This work manifests the value of the DL-based method for the upscaling of detailed DFM to the dual-porosity model and can be extended to construct generalized dual-porosity, dual-permeability models or include more complex physics, such as capillary and gravity effects.

Successful Application of Honey-Bee Optimization Technique in Reservoir Engineering Assisted History Matching: Case Study

2021 ◽  
Author(s):  
Mohamed Shams
Keyword(s):  
Simulation Model ◽  
Reservoir Simulation ◽  
Optimization Algorithm ◽  
History Matching ◽  
Optimization Technique ◽  
Food Sources ◽  
Bee Colony ◽  
Assisted History Matching ◽  
Bee Colony Optimization ◽  
Reservoir Simulation Model

Abstract This paper provides the field application of the bee colony optimization algorithm in assisting the history match of a real reservoir simulation model. Bee colony optimization algorithm is an optimization technique inspired by the natural optimization behavior shown by honeybees during searching for food. The way that honeybees search for food sources in the vicinity of their nest inspired computer science researchers to utilize and apply same principles to create optimization models and techniques. In this work the bee colony optimization mechanism is used as the optimization algorithm in the assisted the history matching workflow applied to a reservoir simulation model of WD-X field producing since 2004. The resultant history matched model is compared with with those obtained using one the most widely applied commercial AHM software tool. The results of this work indicate that using the bee colony algorithm as the optimization technique in the assisted history matching workflow provides noticeable enhancement in terms of match quality and time required to achieve a reasonable match.

Semiquantitative 4D seismic interpretation integrated with reservoir simulation: Application to the Norne field

2018 ◽  
Vol 6 (3) ◽  
pp. T601-T611
Author(s):  
Juliana Maia Carvalho dos Santos ◽  
Alessandra Davolio ◽  
Denis Jose Schiozer ◽  
Colin MacBeth
Keyword(s):  
Simulation Model ◽  
Reservoir Simulation ◽  
History Matching ◽  
Time Lapse ◽  
Seismic Signal ◽  
Seismic Interpretation ◽  
Production Forecast ◽  
Seismic Acquisition ◽  
Reservoir Simulation Model ◽  
4D Seismic

Time-lapse (or 4D) seismic attributes are extensively used as inputs to history matching workflows. However, this integration can potentially bring problems if performed incorrectly. Some of the uncertainties regarding seismic acquisition, processing, and interpretation can be inadvertently incorporated into the reservoir simulation model yielding an erroneous production forecast. Very often, the information provided by 4D seismic can be noisy or ambiguous. For this reason, it is necessary to estimate the level of confidence on the data prior to its transfer to the simulation model process. The methodology presented in this paper aims to diagnose which information from 4D seismic that we are confident enough to include in the model. Two passes of seismic interpretation are proposed: the first, intended to understand the character and quality of the seismic data and, the second, to compare the simulation-to-seismic synthetic response with the observed seismic signal. The methodology is applied to the Norne field benchmark case in which we find several examples of inconsistencies between the synthetic and real responses and we evaluate whether these are caused by a simulation model inaccuracy or by uncertainties in the actual observed seismic. After a careful qualitative and semiquantitative analysis, the confidence level of the interpretation is determined. Simulation model updates can be suggested according to the outcome from this analysis. The main contribution of this work is to introduce a diagnostic step that classifies the seismic interpretation reliability considering the uncertainties inherent in these data. The results indicate that a medium to high interpretation confidence can be achieved even for poorly repeated data.

scholarly journals Effect of initial water flooding on the performance of polymer flooding for heavy oil production

2020 ◽  
Vol 75 ◽  
pp. 19 ◽  
Author(s):  
Clement Fabbri ◽  
Romain de-Loubens ◽  
Arne Skauge ◽  
Gerald Hamon ◽  
Marcel Bourgeois
Keyword(s):  
Oil Recovery ◽  
Heavy Oil ◽  
History Matching ◽  
Oil Production ◽  
Water Flooding ◽  
Relative Permeabilities ◽  
Oil Viscosity ◽  
Initial Water ◽  
Polymer Injection ◽  
Polymer Flood

In the domain of heavy to extra heavy oil production, viscous polymer may be injected after water injection (tertiary mode), or as an alternative (secondary mode) to improve the sweep efficiency and increase oil recovery. To prepare field implementation, nine polymer injection experiments in heavy oil have been performed at core scale, to assess key modelling parameters in both situations. Among this consistent set of experiments, two have been performed on reconstituted cylindrical sandpacks in field-like conditions, and seven on consolidated Bentheimer sandstone in laboratory conditions. All experiments target the same oil viscosity, between 2000 cP and 7000 cP, and the viscosity of Partially Hydrolyzed Polyacrylamide solutions (HPAM 3630) ranges from 60 cP to 80 cP. Water and polymer front propagation are studied using X-ray and tracer measurements. The new experimental results presented here for water flood and polymer flood experiments are compared with experiments described in previous papers. The effects of geometry, viscosity ratio, injection sequence on recoveries, and history match parameters are investigated. Relative permeabilities of the water flood experiment are in line with previous experiments in linear geometry. Initial water floods led to recoveries of 15–30% after one Pore Volume Injected (PVI), a variation influenced by boundary conditions, viscosity, and velocities. The secondary polymer flood in consolidated sandstone confirms less stable displacement than tertiary floods in same conditions. Comparison of secondary and tertiary polymer floods history matching parameters suggests two mechanisms. First, hysteresis effect during oil bank mobilization stabilizes the tertiary polymer front; secondly, the propagation of polymer at higher oil saturation leads to lower adsorption during secondary experiment, generating a lower Residual Resistance Factor (RRF), close to unity. Finally, this paper discusses the use of the relative permeabilities and polymer properties estimated using Darcy equation for field simulation, depending on water distribution at polymer injection start-up.

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