scholarly journals Ensemble-Based Multiobjective Optimization of On/Off Control Devices Under Geological Uncertainty

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
Keyword(s):  
Control Problem ◽  
Net Present Value ◽  
Water Saturation ◽  
Computational Cost ◽  
Production Optimization ◽  
Discrete Control ◽  
Real Field ◽  
Continuous Control ◽  
Initial Water ◽  
Control Devices

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.

Hierarchical Multiscale Methods for Life-Cycle-Production Optimization: A Field Case Study

SPE Journal ◽  
2015 ◽  
Vol 20 (05) ◽  
pp. 896-907 ◽  
Author(s):  
D. F. Oliveira ◽  
A. C. Reynolds
Keyword(s):  
Net Present Value ◽  
Simple Procedure ◽  
Optimization Techniques ◽  
Multiscale Methods ◽  
Production Optimization ◽  
Real Field ◽  
Historical Period ◽  
Reference Case ◽  
Multiscale Optimization ◽  
Hierarchical Multiscale

Summary We apply hierarchical multiscale techniques previously developed by the authors to estimate the well controls that maximize the net present value of the long-term production from a real field offshore Brazil. This field has been in production for several years, and it represents a significant share of the overall oil production for the country. The production-optimization step is preceded by a 10-year historical period, where seismic and production data were history matched by use of ensemble-based approaches. The well controls on a sequence of control steps (time intervals) are optimized for the next 10 years of production by use of the hierarchical-multiscale-optimization and the refinement-indicator-based hierarchical-multiscale-optimization techniques, which refine the control steps as the optimization proceeds. The performance of our approaches is compared with that of a reference case, which applies the well rates used to forecast the production of the real field, as well as with the performance of a standard optimization procedure that uses a fixed set of well controls and a simple procedure to refine control steps.

scholarly journals Production Optimization Based on Integrated Modeling and Gradient Methods

2020 ◽  
Vol 11 (1) ◽  
pp. 7665-7676
Keyword(s):  
Large Scale ◽  
Net Present Value ◽  
Computational Cost ◽  
Gradient Methods ◽  
Optimal Strategies ◽  
Minimum Cost ◽  
Production Optimization ◽  
Oil Field ◽  
Base Case ◽  
Integrated Simulation

Hydrocarbon production from a large-scale field requires optimal strategies in order to sustain returns. However, the determination of optimal plans for production is a challenging task due to high computational cost and various nonlinear constraints. Therefore, an efficient methodology is required to determine the optimum criteria with minimum cost and maximum precision. In this paper, two quasi-newton second-order algorithms of the Broyden–Fletcher–Goldfarb–Shanno (BFGS) and symmetric rank one (SR1) are used to maximize the net-present-value (NPV) over 17 years of production and injection. The framework is applied to an integrated simulation model of reservoir, wells, and surface facilities of a real oil field located in the Middle East. The Lagrangian method is used to combine the objective function and constraints during optimization. The results obtained are compared with those of the current production scheduled plan provided by the oil industry. The performance of algorithms is affected by the production optimization time scale. For the first 12 years of production optimization, SR1 outperform BFGS. For the last 5 years, BFGS outperforms SR1. However, for all years of production, BFGS is superior over SR1 and increases NPV over the base case NPV from 1.605% to 4.92% for various time steps of production.

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 ◽  
...  
Keyword(s):  
Surrogate Model ◽  
History Matching ◽  
Water Saturation ◽  
Computational Cost ◽  
Input Image ◽  
Image Resolution ◽  
Production Optimization ◽  
Reservoir Properties ◽  
Convolutional Network ◽  
Convolutional Encoder

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.

Finite element analysis of the constrained Dirichlet boundary control problem governed by the diffusion problem

2020 ◽  
Vol 26 ◽  
pp. 78
Author(s):  
Thirupathi Gudi ◽  
Ramesh Ch. Sau
Keyword(s):  
Finite Element ◽  
Control Problem ◽  
Dirichlet Boundary ◽  
A Priori ◽  
Diffusion Problem ◽  
Discrete Control ◽  
Optimal Order ◽  
Control Constraints ◽  
Element Analysis ◽  
Dirichlet Boundary Control

We study an energy space-based approach for the Dirichlet boundary optimal control problem governed by the Laplace equation with control constraints. The optimality system results in a simplified Signorini type problem for control which is coupled with boundary value problems for state and costate variables. We propose a finite element based numerical method using the linear Lagrange finite element spaces with discrete control constraints at the Lagrange nodes. The analysis is presented in a combination for both the gradient and the L2 cost functional. A priori error estimates of optimal order in the energy norm is derived up to the regularity of the solution for both the cases. Theoretical results are illustrated by some numerical experiments.

A Unified Parameter to Represent Reservoir Heterogeneity

2021 ◽  
Author(s):  
Nandana Ramabhadra Agastya
Keyword(s):  
Water Saturation ◽  
Pressure Ratio ◽  
Universal Method ◽  
Permeability Ratio ◽  
Initial Water ◽  
Vertical Permeability ◽  
Radar Chart ◽  
Horizontal Permeability ◽  
Water Cut ◽  
Thief Zone

Abstract We aim to find a universal method and/or parameter to quantify impact of overall heterogeneity on waterflood performance. For this purpose, we combined the Lorenz coefficient, horizontal permeability to vertical permeability ratio, and thief zone permeability to average permeability ratio, with a radar chart. The area of the radar chart serves as a single parameter to rank reservoirs according to heterogeneity, and correlates to waterflood performance. The parameters investigated are vertical and horizontal permeability. Average porosity, initial water saturation, and initial diagonal pressure ratio are kept constant. Computer based experiments are used over the course of this entire research. We conducted permeability studies that demonstrate the effects of thief zones and crossflow. After normalizing these parameters into a number between 0 and 1, we then plot them on a radar chart. A reservoir's overall degree of heterogeneity can be inferred using the radar chart area procedure discussed in this study. In general, our simulations illustrate that the larger the radar chart area, the more heterogenous the reservoir is, which in turn yields higher water cut trends and lower recovery factors. Computer simulations done during this study also show that the higher the Lorenz coefficient, the higher the probability of a thief zone to exist. Simulations done to study crossflow also show certain trends with respect to under tonguing and radar chart area.

Streamline Tracing and Applications in Dual Porosity Dual Permeability Models

2021 ◽  
Author(s):  
Tsubasa Onishi ◽  
Hongquan Chen ◽  
Jiang Xie ◽  
Shusei Tanaka ◽  
Dongjae Kam ◽  
...  
Keyword(s):  
History Matching ◽  
Net Present Value ◽  
Time Of Flight ◽  
Rate Allocation ◽  
Production Optimization ◽  
Dual Porosity ◽  
Computationally Efficient ◽  
Present Value ◽  
Sweep Efficiency ◽  
Streamline Tracing

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.

Comparison of Various Discretization Schemes for Simulation of Large Field Case Reservoirs Using Unstructured Grids

2021 ◽  
Author(s):  
Samier Pierre ◽  
Raguenel Margaux ◽  
Darche Gilles
Keyword(s):  
Large Scale ◽  
Unstructured Grids ◽  
Computational Cost ◽  
Large Field ◽  
Real Field ◽  
Test Case ◽  
Generation Task ◽  
Field Case ◽  
Flux Approximation ◽  
Discretization Schemes

Abstract Solving the equations governing multiphase flow in geological formations involves the generation of a mesh that faithfully represents the structure of the porous medium. This challenging mesh generation task can be greatly simplified by the use of unstructured (tetrahedral) grids that conform to the complex geometric features present in the subsurface. However, running a million-cell simulation problem using an unstructured grid on a real, faulted field case remains a challenge for two main reasons. First, the workflow typically used to construct and run the simulation problems has been developed for structured grids and needs to be adapted to the unstructured case. Second, the use of unstructured grids that do not satisfy the K-orthogonality property may require advanced numerical schemes that preserve the accuracy of the results and reduce potential grid orientation effects. These two challenges are at the center of the present paper. We describe in detail the steps of our workflow to prepare and run a large-scale unstructured simulation of a real field case with faults. We perform the simulation using four different discretization schemes, including the cell-centered Two-Point and Multi-Point Flux Approximation (respectively, TPFA and MPFA) schemes, the cell- and vertex-centered Vertex Approximate Gradient (VAG) scheme, and the cell- and face-centered hybrid Mimetic Finite Difference (MFD) scheme. We compare the results in terms of accuracy, robustness, and computational cost to determine which scheme offers the best compromise for the test case considered here.

scholarly journals Estimating reservoir permeability with borehole radar

Geophysics ◽  
2020 ◽  
Vol 85 (4) ◽  
pp. H51-H60
Author(s):  
Feng Zhou ◽  
Iraklis Giannakis ◽  
Antonios Giannopoulos ◽  
Klaus Holliger ◽  
Evert Slob
Keyword(s):  
Water Saturation ◽  
Time Lapse ◽  
Drilling Mud ◽  
Invasion Depth ◽  
Initial Water ◽  
Oil Drilling ◽  
Reservoir Evaluation ◽  
Reservoir Permeability ◽  
Borehole Radar ◽  
Porosity And Permeability

In oil drilling, mud filtrate penetrates into porous formations and alters the compositions and properties of the pore fluids. This disturbs the logging signals and brings errors to reservoir evaluation. Drilling and logging engineers therefore deem mud invasion as undesired and attempt to eliminate its adverse effects. However, the mud-contaminated formation carries valuable information, notably with regard to its hydraulic properties. Typically, the invasion depth critically depends on the formation porosity and permeability. Therefore, if adequately characterized, mud invasion effects could be used for reservoir evaluation. To pursue this objective, we have applied borehole radar to measure mud invasion depth considering its high radial spatial resolution compared with conventional logging tools, which then allows us to estimate the reservoir permeability based on the acquired invasion depth. We investigate the feasibility of this strategy numerically through coupled electromagnetic and fluid modeling in an oil-bearing layer drilled using freshwater-based mud. Time-lapse logging is simulated to extract the signals reflected from the invasion front, and a dual-offset downhole antenna mode enables time-to-depth conversion to determine the invasion depth. Based on drilling, coring, and logging data, a quantitative interpretation chart is established, mapping the porosity, permeability, and initial water saturation into the invasion depth. The estimated permeability is in a good agreement with the actual formation permeability. Our results therefore suggest that borehole radar has significant potential to estimate permeability through mud invasion effects.

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