Prediction of Field Saturations Using a Fully Convolutional Network Surrogate

SPE Journal ◽  
2021 ◽  
pp. 1-13
Author(s):  
Kai Zhang ◽  
Yanzhong Wang ◽  
Guoxin Li ◽  
Xiaopeng Ma ◽  
Shiti Cui ◽  
...  

Summary We are interested in the development of surrogate models for the prediction of field saturations using a fully convolutional encoder/decoder network based on the dense convolutional network (DenseNet; Huang et al. 2017), similar to the approaches used for image/image-regression tasks in deep learning. In the surrogate model, the encoder network automatically extracts the multiscale features from the raw input data, and the decoder network then uses these data to recover the input image resolution at the output of the model. The input of multiple influencing factors is considered to make our surrogate model more consistent with the physical laws, which has achieved good results in the prediction of output fields in our experiments. Various reservoir parameters including the static reservoir properties (i.e., permeability field) and dynamic reservoir properties (i.e., well placement) are used as input features, and the water-saturation distributions in different periods are taken as the output. Compared with traditional numerical reservoir simulation, which has a high computational cost and is time consuming, not only does it present the same precision, but it costs less time. At the same time, it can also be used for production optimization and history matching.

2015 ◽  
Vol 18 (04) ◽  
pp. 554-563 ◽  
Author(s):  
R.-M.. -M. Fonseca ◽  
O.. Leeuwenburgh ◽  
E.. Della Rossa ◽  
P. M. Van den Hof ◽  
J.-D.. -D. Jansen

Summary We consider robust ensemble-based (EnOpt) multiobjective production optimization of on/off inflow-control devices (ICDs) for a sector model inspired by a real-field case. The use of on/off valves as optimization variables leads to a discrete control problem. We propose a reparameterization of such discrete controls in terms of switching times (i.e., we optimize the time at which a particular valve is either open or closed). This transforms the discrete control problem into a continuous control problem that can be efficiently handled with the EnOpt method. In addition, this leads to a significant reduction in the number of controls that is expected to be beneficial for gradient quality when using approximate gradients. We consider an ensemble of sector models where the uncertainty is described by different permeability, porosity, net/gross ratios, and initial water-saturation fields. The controls are the ICD settings over time in the three horizontal injection wells, with approximately 15 ICDs per well. Different optimized strategies resulting from different initial strategies were compared. We achieved a mean 4.2% increase in expected net present value (NPV) at a 10% discount rate compared with a traditional pressure-maintenance strategy. Next, we performed a sequential biobjective optimization and achieved an increase of 9.2% in the secondary objective (25% discounted NPV to emphasize short-term production gains) for a minimal decrease of 1% in the primary objective (0% discounted NPV to emphasize long-term recovery gains), as averaged over the 100 geological realizations. The work flow was repeated for alternative numbers of ICDs, showing that having fewer control options lowers the expected value for this particular case. The results demonstrate that ensemble-based optimization work flows are able to produce improved robust recovery strategies for realistic field-sector models against acceptable computational cost.


Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 991
Author(s):  
Yuta Nakahara ◽  
Toshiyasu Matsushima

In information theory, lossless compression of general data is based on an explicit assumption of a stochastic generative model on target data. However, in lossless image compression, researchers have mainly focused on the coding procedure that outputs the coded sequence from the input image, and the assumption of the stochastic generative model is implicit. In these studies, there is a difficulty in discussing the difference between the expected code length and the entropy of the stochastic generative model. We solve this difficulty for a class of images, in which they have non-stationarity among segments. In this paper, we propose a novel stochastic generative model of images by redefining the implicit stochastic generative model in a previous coding procedure. Our model is based on the quadtree so that it effectively represents the variable block size segmentation of images. Then, we construct the Bayes code optimal for the proposed stochastic generative model. It requires the summation of all possible quadtrees weighted by their posterior. In general, its computational cost increases exponentially for the image size. However, we introduce an efficient algorithm to calculate it in the polynomial order of the image size without loss of optimality. As a result, the derived algorithm has a better average coding rate than that of JBIG.


Water ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 87
Author(s):  
Yongqiang Wang ◽  
Ye Liu ◽  
Xiaoyi Ma

The numerical simulation of the optimal design of gravity dams is computationally expensive. Therefore, a new optimization procedure is presented in this study to reduce the computational cost for determining the optimal shape of a gravity dam. Optimization was performed using a combination of the genetic algorithm (GA) and an updated Kriging surrogate model (UKSM). First, a Kriging surrogate model (KSM) was constructed with a small sample set. Second, the minimizing the predictor strategy was used to add samples in the region of interest to update the KSM in each updating cycle until the optimization process converged. Third, an existing gravity dam was used to demonstrate the effectiveness of the GA–UKSM. The solution obtained with the GA–UKSM was compared with that obtained using the GA–KSM. The results revealed that the GA–UKSM required only 7.53% of the total number of numerical simulations required by the GA–KSM to achieve similar optimization results. Thus, the GA–UKSM can significantly improve the computational efficiency. The method adopted in this study can be used as a reference for the optimization of the design of gravity dams.


2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Yang Yu ◽  
Hongqing Zhu

AbstractDue to the complex morphology and characteristic of retinal vessels, it remains challenging for most of the existing algorithms to accurately detect them. This paper proposes a supervised retinal vessels extraction scheme using constrained-based nonnegative matrix factorization (NMF) and three dimensional (3D) modified attention U-Net architecture. The proposed method detects the retinal vessels by three major steps. First, we perform Gaussian filter and gamma correction on the green channel of retinal images to suppress background noise and adjust the contrast of images. Then, the study develops a new within-class and between-class constrained NMF algorithm to extract neighborhood feature information of every pixel and reduce feature data dimension. By using these constraints, the method can effectively gather similar features within-class and discriminate features between-class to improve feature description ability for each pixel. Next, this study formulates segmentation task as a classification problem and solves it with a more contributing 3D modified attention U-Net as a two-label classifier for reducing computational cost. This proposed network contains an upsampling to raise image resolution before encoding and revert image to its original size with a downsampling after three max-pooling layers. Besides, the attention gate (AG) set in these layers contributes to more accurate segmentation by maintaining details while suppressing noises. Finally, the experimental results on three publicly available datasets DRIVE, STARE, and HRF demonstrate better performance than most existing methods.


2021 ◽  
pp. 014459872199465
Author(s):  
Yuhui Zhou ◽  
Sheng Lei ◽  
Xuebiao Du ◽  
Shichang Ju ◽  
Wei Li

Carbonate reservoirs are highly heterogeneous. During waterflooding stage, the channeling phenomenon of displacing fluid in high-permeability layers easily leads to early water breakthrough and high water-cut with low recovery rate. To quantitatively characterize the inter-well connectivity parameters (including conductivity and connected volume), we developed an inter-well connectivity model based on the principle of inter-well connectivity and the geological data and development performance of carbonate reservoirs. Thus, the planar water injection allocation factors and water injection utilization rate of different layers can be obtained. In addition, when the proposed model is integrated with automatic history matching method and production optimization algorithm, the real-time oil and water production can be optimized and predicted. Field application demonstrates that adjusting injection parameters based on the model outputs results in a 1.5% increase in annual oil production, which offers significant guidance for the efficient development of similar oil reservoirs. In this study, the connectivity method was applied to multi-layer real reservoirs for the first time, and the injection and production volume of injection-production wells were repeatedly updated based on multiple iterations of water injection efficiency. The correctness of the method was verified by conceptual calculations and then applied to real reservoirs. So that the oil field can increase production in a short time, and has good application value.


2001 ◽  
Vol 4 (06) ◽  
pp. 455-466 ◽  
Author(s):  
A. Graue ◽  
T. Bognø ◽  
B.A. Baldwin ◽  
E.A. Spinler

Summary Iterative comparison between experimental work and numerical simulations has been used to predict oil-recovery mechanisms in fractured chalk as a function of wettability. Selective and reproducible alteration of wettability by aging in crude oil at an elevated temperature produced chalk blocks that were strongly water-wet and moderately water-wet, but with identical mineralogy and pore geometry. Large scale, nuclear-tracer, 2D-imaging experiments monitored the waterflooding of these blocks of chalk, first whole, then fractured. This data provided in-situ fluid saturations for validating numerical simulations and evaluating capillary pressure- and relative permeability-input data used in the simulations. Capillary pressure and relative permeabilities at each wettability condition were measured experimentally and used as input for the simulations. Optimization of either Pc-data or kr-curves gave indications of the validity of these input data. History matching both the production profile and the in-situ saturation distribution development gave higher confidence in the simulations than matching production profiles only. Introduction Laboratory waterflood experiments, with larger blocks of fractured chalk where the advancing waterfront has been imaged by a nuclear tracer technique, showed that changing the wettability conditions from strongly water-wet to moderately water-wet had minor impact on the the oil-production profiles.1–3 The in-situ saturation development, however, was significantly different, indicating differences in oil-recovery mechanisms.4 The main objective for the current experiments was to determine the oil-recovery mechanisms at different wettability conditions. We have reported earlier on a technique that reproducibly alters wettability in outcrop chalk by aging the rock material in stock-tank crude oil at an elevated temperature for a selected period of time.5 After applying this aging technique to several blocks of chalk, we imaged waterfloods on blocks of outcrop chalk at different wettability conditions, first as a whole block, then when the blocks were fractured and reassembled. Earlier work reported experiments using an embedded fracture network,4,6,7 while this work also studied an interconnected fracture network. A secondary objective of these experiments was to validate a full-field numerical simulator for prediction of the oil production and the in-situ saturation dynamics for the waterfloods. In this process, the validity of the experimentally measured capillary pressure and relative permeability data, used as input for the simulator, has been tested at strongly water-wet and moderately water-wet conditions. Optimization of either Pc data or kr curves for the chalk matrix in the numerical simulations of the whole blocks at different wettabilities gave indications of the data's validity. History matching both the production profile and the in-situ saturation distribution development gave higher confidence in the simulations of the fractured blocks, in which only the fracture representation was a variable. Experimental Rock Material and Preparation. Two chalk blocks, CHP8 and CHP9, approximately 20×12×5 cm thick, were obtained from large pieces of Rørdal outcrop chalk from the Portland quarry near Ålborg, Denmark. The blocks were cut to size with a band saw and used without cleaning. Local air permeability was measured at each intersection of a 1×1-cm grid on both sides of the blocks with a minipermeameter. The measurements indicated homogeneous blocks on a centimeter scale. This chalk material had never been contacted by oil and was strongly water-wet. The blocks were dried in a 90°C oven for 3 days. End pieces were mounted on each block, and the whole assembly was epoxy coated. Each end piece contained three fittings so that entering and exiting fluids were evenly distributed with respect to height. The blocks were vacuum evacuated and saturated with brine containing 5 wt% NaCl+3.8 wt% CaCl2. Fluid data are found in Table 1. Porosity was determined from weight measurements, and the permeability was measured across the epoxy-coated blocks, at 2×10–3 µm2 and 4×10–3 µm2, for CHP8 and CHP9, respectively (see block data in Table 2). Immobile water saturations of 27 to 35% pore volume (PV) were established for both blocks by oilflooding. To obtain uniform initial water saturation, Swi, oil was injected alternately at both ends. Oilfloods of the epoxy-coated block, CHP8, were carried out with stock-tank crude oil in a heated pressure vessel at 90°C with a maximum differential pressure of 135 kPa/cm. CHP9 was oilflooded with decane at room temperature. Wettability Alteration. Selective and reproducible alteration of wettability, by aging in crude oil at elevated temperatures, produced a moderately water-wet chalk block, CHP8, with similar mineralogy and pore geometry to the untreated strongly water-wet chalk block CHP9. Block CHP8 was aged in crude oil at 90°C for 83 days at an immobile water saturation of 28% PV. A North Sea crude oil, filtered at 90°C through a chalk core, was used to oilflood the block and to determine the aging process. Two twin samples drilled from the same chunk of chalk as the cut block were treated similar to the block. An Amott-Harvey test was performed on these samples to indicate the wettability conditions after aging.8 After the waterfloods were terminated, four core plugs were drilled out of each block, and wettability measurements were conducted with the Amott-Harvey test. Because of possible wax problems with the North Sea crude oil used for aging, decane was used as the oil phase during the waterfloods, which were performed at room temperature. After the aging was completed for CHP8, the crude oil was flushed out with decahydronaphthalene (decalin), which again was flushed out with n-decane, all at 90°C. Decalin was used as a buffer between the decane and the crude oil to avoid asphalthene precipitation, which may occur when decane contacts the crude oil.


Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 626
Author(s):  
Jiyuan Zhang ◽  
Bin Zhang ◽  
Shiqian Xu ◽  
Qihong Feng ◽  
Xianmin Zhang ◽  
...  

The relative permeability of coal to gas and water exerts a profound influence on fluid transport in coal seams in both primary and enhanced coalbed methane (ECBM) recovery processes where multiphase flow occurs. Unsteady-state core-flooding tests interpreted by the Johnson–Bossler–Naumann (JBN) method are commonly used to obtain the relative permeability of coal. However, the JBN method fails to capture multiple gas–water–coal interaction mechanisms, which inevitably results in inaccurate estimations of relative permeability. This paper proposes an improved assisted history matching framework using the Bayesian adaptive direct search (BADS) algorithm to interpret the relative permeability of coal from unsteady-state flooding test data. The validation results show that the BADS algorithm is significantly faster than previous algorithms in terms of convergence speed. The proposed method can accurately reproduce the true relative permeability curves without a presumption of the endpoint saturations given a small end-effect number of <0.56. As a comparison, the routine JBN method produces abnormal interpretation results (with the estimated connate water saturation ≈33% higher than and the endpoint water/gas relative permeability only ≈0.02 of the true value) under comparable conditions. The proposed framework is a promising computationally effective alternative to the JBN method to accurately derive relative permeability relations for gas–water–coal systems with multiple fluid–rock interaction mechanisms.


2021 ◽  
Author(s):  
Tsubasa Onishi ◽  
Hongquan Chen ◽  
Jiang Xie ◽  
Shusei Tanaka ◽  
Dongjae Kam ◽  
...  

Abstract Streamline-based methods have proven to be effective for various subsurface flow and transport modeling problems. However, the applications are limited in dual-porosity and dual-permeability (DPDK) system due to the difficulty in describing interactions between matrix and fracture during streamline tracing. In this work, we present a robust streamline tracing algorithm for DPDK models and apply the new algorithm to rate allocation optimization in a waterflood reservoir. In the proposed method, streamlines are traced in both fracture and matrix domains. The inter-fluxes between fracture and matrix are described by switching streamlines from one domain to another using a probability computed based on the inter-fluxes. The approach is fundamentally similar to the existing streamline tracing technique and can be utilized in streamline-assisted applications, such as flow diagnostics, history matching, and production optimization. The proposed method is benchmarked with a finite-volume based approach where grid-based time-of-flight was obtained by solving the stationary transport equation. We first validated our method using simple examples. Visual time-of-flight comparisons as well as tracer concentration and allocation factors at wells show good agreement. Next, we applied the proposed method to field scale models to demonstrate the robustness. The results show that our method offers reduced numerical artifacts and better represents reservoir heterogeneity and well connectivity with sub-grid resolutions. The proposed method is then used for rate allocation optimization in DPDK models. A streamline-based gradient free algorithm is used to optimize net present value by adjusting both injection and production well rates under operational constraints. The results show that the optimized schedule offers significant improvement in recovery factor, net present value, and sweep efficiency compared to the base scenario using equal rate injection and production. The optimization algorithm is computationally efficient as it requires only a few forward reservoir simulations.


2021 ◽  
pp. 1-29
Author(s):  
Eric Sonny Mathew ◽  
Moussa Tembely ◽  
Waleed AlAmeri ◽  
Emad W. Al-Shalabi ◽  
Abdul Ravoof Shaik

Two of the most critical properties for multiphase flow in a reservoir are relative permeability (Kr) and capillary pressure (Pc). To determine these parameters, careful interpretation of coreflooding and centrifuge experiments is necessary. In this work, a machine learning (ML) technique was incorporated to assist in the determination of these parameters quickly and synchronously for steady-state drainage coreflooding experiments. A state-of-the-art framework was developed in which a large database of Kr and Pc curves was generated based on existing mathematical models. This database was used to perform thousands of coreflood simulation runs representing oil-water drainage steady-state experiments. The results obtained from the corefloods including pressure drop and water saturation profile, along with other conventional core analysis data, were fed as features into the ML model. The entire data set was split into 70% for training, 15% for validation, and the remaining 15% for the blind testing of the model. The 70% of the data set for training teaches the model to capture fluid flow behavior inside the core, and then 15% of the data set was used to validate the trained model and to optimize the hyperparameters of the ML algorithm. The remaining 15% of the data set was used for testing the model and assessing the model performance scores. In addition, K-fold split technique was used to split the 15% testing data set to provide an unbiased estimate of the final model performance. The trained/tested model was thereby used to estimate Kr and Pc curves based on available experimental results. The values of the coefficient of determination (R2) were used to assess the accuracy and efficiency of the developed model. The respective crossplots indicate that the model is capable of making accurate predictions with an error percentage of less than 2% on history matching experimental data. This implies that the artificial-intelligence- (AI-) based model is capable of determining Kr and Pc curves. The present work could be an alternative approach to existing methods for interpreting Kr and Pc curves. In addition, the ML model can be adapted to produce results that include multiple options for Kr and Pc curves from which the best solution can be determined using engineering judgment. This is unlike solutions from some of the existing commercial codes, which usually provide only a single solution. The model currently focuses on the prediction of Kr and Pc curves for drainage steady-state experiments; however, the work can be extended to capture the imbibition cycle as well.


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