R. Bratvold

University of Stavanger

 

Today’s decision makers in the oil and gas industry are faced with remarkable new technologies, huge amounts of information to help them make high-quality decisions, and the ability to share information at unprecedented speeds and quantities. In this talk we will cover two related topics.

 

1. The use of Bayesian Decision Networks (BDN) to structure drilling decision problems, show the relevance between the key uncertainties and decisions, and consistently update information with the arrival of new data. The BDN provides a methodology for robust (in both the conceptual framework and algorithm performance) decision support in drilling operations.

 

2. The use of a decision analytic approach to assess the value of Integrated operations (IO), and/or Smart Fields applications. A central element in the valuation is the capturing of the interplay between information revelation and decision making over time and the approach will not only provide a value of IO for the specific decision at hand but will also include a policy map indicating the value add of each IO component.

 

H. Wang

Stanford University

 

Optimizing a stochastic system with a set of discrete design variables x is an important and difficult problem arising widely in various fields of operations research and the management sciences. Much research has originated methods for discrete stochastic optimization problems in which the objective function g is defined by a Monte Carlo simulation oracle. The function g is implicit in the oracle, i.e., for any design point x the objective value g(x) can be obtained only asymptotically by averaging over many calls to the oracle.

 

Our interest is in integer decision variables x when the objective function g is smooth, in the sense that if "viewed from a distance" the discreteness is negligible. Such applications arise, for example, when the decision variables are inventory reorder points and quantities, numbers of machines, numbers of stock options, or staffing levels. Such smoothness implies that a local search on g can be successful in finding a good solution. Many global random search methods also could be used for such problems, but their generality makes them inefficient compared to local-search approaches.

 

We propose a family of simulation-based retrospective optimization algorithms for large-scale discrete stochastic systems via piecewise-linear interpolation. With a simplicial linear interpolation we create a continuous response surface for a discrete feasible region. A retrospective framework generates a sequence of deterministic sample-path problems that can be solved using deterministic nonlinear optimization techniques. Numerical experiments show that our method finds good estimates of optimal solutions for inventory control and transportation and scheduling systems, significantly faster than other state-of-the-art schemes. Some examples, including well placement optimization problems, are discussed in the talk.

 

O. Kramer

TU Dortmund

 

Evolutionary computation comprises stochastic methods for global optimization, i.e., optimization problems with multiple local optima. They are biologically inspired and imitate principles that can be observed in natural evolution like mutation, crossover and selection. Evolutionary methods are direct search methods, i.e., they can be applied in solution spaces where no derivatives are available. The advanced methods are designed for special solution space conditions such as multimodal, constrained and multi-objective fitness landscapes. The talk puts the focus on covariance matrix self-adaptation evolution strategies that are based on the automatic control of step sizes to adapt to local solution space conditions. Many problems are subject to constraints that make a hard problem even harder. A constraint handling method for covariance matrix self-adaptation is presented that is based on estimating meta-models of the constraint boundary with machine learning methods. If multiple conflictive objectives have to be optimized at the same time, evolutionary multi-objective optimization methods deliver the Pareto set of balanced solutions. The talk introduces rake selection, a new method that approximates uniformly distributed solutions on the Pareto-front. Perspectives are shown where these stochastic methods can solve optimization problems in the oil and gas industry.

 

D.A. Cameron and L.J. Durlofsky

Stanford University

 

One way to reduce anthropogenic carbon emissions may be to capture carbon dioxide produced in power plants and store it in geological structures. This research treats the injection of CO2 in deep brine aquifers as an optimal control problem, for which, the injection parameters are controlled with respect to maximizing safety and minimizing cost. The optimizations are carried out on a 3D, synthetic, heterogeneous model. Two possible objective functions are considered: 1) the fraction of mobile CO2 after equilibration; and 2) the fraction of CO2 stored immediately underneath the cap-rock after equilibration. We compare several low-level optimization algorithms including general pattern search (GPS), mesh-adaptive pattern search (MADS) and some simple gradient methods. Preliminary results indicate that optimized solutions perform significantly better than if one were to inject an equal volume into each well. Furthermore, we have found that GPS is the best method tested, and that large improvements can be made to the optimal solution if more than one update to the control is used.

 

J.E. Onwunalu and L.J. Durlofsky

Stanford University

 

Determining the optimum type and location of new wells is an essential component in the efficient development of oil and gas fields. The optimization problem is, however, demanding due to the potentially high dimension of the search space and the computational requirements associated with function evaluations, which in this case entail full reservoir simulations.

 

In this talk the Particle Swarm Optimization (PSO) algorithm is applied for the determination of optimal well type and location. The PSO algorithm is a stochastic procedure that uses a population of solutions, called particles, which move in the search space. Particle positions are updated iteratively according to particle fitness (objective function value) and position relative to other particles. The general PSO procedure is first discussed, and then the particular variant implemented for well optimization is described. Some examples are shown. These involve vertical, deviated and dual-lateral wells and optimization over single and multiple reservoir realizations. For each case, both the PSO algorithm and the widely used genetic algorithm (GA) are applied to maximize net present value. Multiple runs of both algorithms are performed and the results are averaged in order to achieve meaningful comparisons. It is observed that, on average, PSO outperforms GA in all cases considered, though the relative advantages of PSO vary from case to case. Taken in total these findings are very promising and demonstrate the applicability of PSO for this challenging problem.

 

A. Awotunde and R.N. Horne

Stanford University

 

An important issue in reservoir parameter estimation is to develop computationally efficient and reliable nonlinear regression procedures. We present a multiresolution wavelet approach to estimate spatial distribution of reservoir parameters. By performing the nonlinear least squares procedure completely in the wavelet domain we achieve proper data integration. Wavelet transforms have the ability to reveal important events in any signal or image. Thus we transformed the model space and the data time into spatial wavelet and time wavelet domains and used a thresholding to select a subset of wavelet coefficients from each of the transformed domains. These subsets were subsequently used in the nonlinear regression procedure to estimate the appropriate set of reservoir parameters.

 

We applied the procedure to a radial composite reservoir system to demonstrate the reliability of the approach. The test case reservoir was composed of unknown permeability values distributed in the radial field. The inverse problem was solved to estimate the distributed permeability values by performing the nonlinear least square regression in the wavelet domains (time and space).

 

Results obtained were compared to those obtained from conventional nonlinear regression approach. The time-space wavelet approach proves to be computationally more efficient compared to the conventional approach. By reducing the dimensions of the model and data spaces the model stabilizes the algorithm and gives faster convergence. Significantly, the approach reveals the true number of reservoir parameters that can be appropriately estimated from a given data set.

 

J.L. Fernández Martínez

University of Oviedo

 

Geophysical inverse problems are ill-posed: in geoelectrical inverse problems the error function has its minimum in a flat elongated valley or surrounded by many local minima. Local optimization methods give unpredictable results if no prior information is available. Traditionally this has generated mistrust in the use of geophysical inverse methods (equivalence problem in Vertical Electrical Soundings). Stochastic approach of inverse problems consists in shifting attention to the probability of existence of certain interesting subsurface structures instead of "looking for the true model". Also, inverse problems are ill-conditioned and observed data are noisy. Thus, with no regularization methods (priors) these uncertainties are transmitted to the model parameters by the optimization algorithm. Global optimization methods had become a good alternative to sample efficiently the model space. They are very robust since they do not solve the optimization problem.

 

In this seminar I will talk about our research on Particle Swarm Optimization (PSO) to solve efficiently geophysical inverse problems. The PSO algorithm can be physically interpreted as a stochastic damped mass-spring system. This analogy served us to introduce the PSO continuous model and to deduce a whole family of PSO algorithms arising from different discretizations of the PSO continuous model. The performance of these methods can be checked using synthetic functions showing a degree of ill-posedness similar to that found in real problems. Finally, I will be focused on two different low-cost methods in hydrogeophysics: 1) a water intrusion study using Vertical Electrical Soundings (VES), 2) the inversion of SP data in hydrogeophysics.

 

Y. Arogunmati

Stanford University

 

I am proposing an approach to quasi-continuous, geophysical monitoring of sequestered CO2 in geological reservoirs with sparse seismic data. This approach, called data evolution, takes advantage of the small amount of change in the seismic property of a geological reservoir that occurs in a small time interval during or after injection of CO2. The goal of this approach is to obtain high temporal and spatial resolution in reconstructed, time-lapse geophysical models using the same resources that would have provided high spatial but low temporal resolution. This is done by acquiring sparse data at small time intervals. In this case, a sparse dataset refers to that dataset which is a small fraction (as little as 5%) of what would be required to reconstruct a high spatial resolution image of the subsurface. The high spatial resolution obtained by the proposed approach occurs because unmeasured data are estimated from future and past data. With high temporal and spatial resolution, early detection of leaks in a sequestered CO2 reservoir is guaranteed. This approach comprises two key steps: a missing data estimation step, which converts the sparse data to full data, and then a geophysical data inversion step, which is used to reconstruct the geophysical model.

 

V. Pereyra

Weidlinger Associates (Retired)

 

Many optimization problems in science and engineering have multiple objectives. Often these problems are solved by converting them to single objective constrained ones, which allows the use of familiar tools. However, this conversion can lead to sub-optimal results since it usually does not explore the whole space of solutions of the original problem.

 

In this presentation we will introduce the basic theory of optimality for multiobjective optimization problems and describe methods for sampling adequately the space of solutions, the so called Pareto front.

 

An interesting application of these ideas to problems in Reservoir Optimization could be in the area of cooperative inversion, when multiple data sets (seismic, pressure histories, electromagnetic, etc.) are blended together to better image material properties. Although it is natural to think that the solutions of different inversion problems with common parameters will lead to consistent results, this is hardly the case, since ill-conditioning, insufficient data and non-linearities, usually make these problems very hard to solve.

 

Blending all the data into a single objective using some weights is one of the favorite methods. Unfortunately, this just changes the problem to having to select the weights, since different weights lead to different solutions (in fact, different Pareto optimal points!).

 

M.A. Abramson (1), C. Audet (2), J. Dennis (3) and S. Le Digabel (2)

(1) The Boeing Company

(2) Ecole Polytechnique de Montreal

(3) Rice University and University of Washington

 

This is ongoing work towards our goal of providing effective algorithms for derivative-free blackbox nonlinear programming. The class of problems we target are evaluated by calling "blackbox" computer codes. Often, some constraints are much cheaper to evaluate than others and some of the constraints only return "yes" if they are satisfied and "no" if they are not. Some constraints must be satisfied or the objective function and other constraints cannot be evaluated. There are even cases that show up unexpectedly as the solution process proceeds by failing to return a value for the objective function or constraints despite being called with an argument that is feasible with respect to the given constraints.

 

Our class of mesh adaptive direct search (MADS) algorithms has a satisfying convergence analysis for locally Lipschitz functions. We discuss those results, and we give a new example, orthoMADS. We give some numerical tests using our "progressive barrier" version orthoMADS-PB. The progressive barrier idea is related to the derivative-free filter approach, and it is available in our NOMAD codes.

 

M. Dadashpour, D. Echeverria Ciaurri, T. Mukerji, J. Kleppe and M. Landrø

Norwegian University of Science and Technology (NTNU) and Stanford University

 

This study presents a method based on the Gauss-Newton optimization technique for continuous reservoir model updating (porosity and permeability) with respect to production history and 4D seismic data (in the form of zero offset amplitudes and AVO gradients). Using only production data or zero offset 4D seismic amplitudes as observation data in the parameter estimation process, cannot properly explore the solution space. Therefore, integration of production data and 4D seismic zero offset amplitudes and AVO gradients, combined with empirical knowledge about rock types from laboratory measurements, is one way to further constrain the inversion process.

 

In the first part of this talk we study the feasibility of integrating data as input to reservoir parameter estimation, and we present results for a semi-synthetic history matching problem extracted from the Norne field in the Norwegian Sea.

 

B. Kaelin

3DGeo Inc.

 

Future development of hydrocarbon resources will include exploration in increasingly complex geological environments, necessitating increasing advances in computationally intensive imaging technologies for both exploration and exploitation.

 

Among these technological advances, reverse time migration (RTM) yields the best possible images. RTM is based on the solution of the two-way acoustic wave-equation. This technique relies on the velocity model to image turning waves. These turning waves are particularly important to unravel subsalt reservoirs and delineate salt-flanks, a natural trap for oil and gas. Because it relies on an accurate velocity model, RTM opens new frontiers in designing better velocity estimation algorithms. The chief impediment to the large-scale, routine deployment of RTM has been a lack of sufficient computational power. RTM needs about thirty times the computing power used in exploration today to be commercially viable and widely used.

 

To overcome these challenges, the Kaleidoscope Project, a partnership between Repsol YPF, Barcelona Supercomputing Center, 3DGeo Inc. and IBM brings together the necessary components of modeling, algorithms and the uniquely powerful computing power of the MareNostrum Supercomputer in Barcelona to realize the promise of RTM, incorporate it into daily processing flows, and to solve exploration problems in a highly cost-effective way. Uniquely, the Kaleidoscope Project is simultaneously integrating software and hardware, steps that are traditionally taken sequentially. This unique integration will accelerate seismic imaging by several orders of magnitude compared to conventional solutions on standard Linux Clusters.

 

E. Delage and Y. Ye

Stanford University

 

Stochastic programs can effectively describe the decision-making problem in an uncertain environment (e.g., optimal management of reservoir floods). Unfortunately, such programs are often computationally demanding to solve. In addition, their solutions can be misleading when there is ambiguity in the choice of a distribution for the random parameters.

 

In this talk, we propose a model describing uncertainty in both the distribution form (discrete, Gaussian, exponential, etc.) and the moments form (mean and covariance). In fact, by deriving new confidence regions for the mean and covariance of a random vector, we provide probabilistic arguments for using our model in problems that rely heavily on historical observations of the parameters. We demonstrate that for a wide range of convex cost functions the associated distributionally robust stochastic program can be solved efficiently. We also consider our framework in the context of more general optimization problems, where our methods can lead to better decisions in terms of risk-adjusted performance with respect to the undetermined environment.

 

J. Caers

Stanford University

 

Traditional to geostatistics is the quantification of spatial variability through a variogram or co-variance model, often within a Multi-Gaussian random field description. Since most practical applications work on a grid, the input of many algorithms is essentially a co-variance table, in the theoretical limit, a large N x N matrix whereby N is the number of cells in the model grid. In most practical applications, N is larger than the number of realizations, N_R, generated. Co-variance-based models are however limited in representing realistic spatial variability, hence recently, multi-point geostatistical methods have been developed to better represent actual spatial variation. The multi-point approach has mostly relied on the construction of efficient algorithms whose application has been successful, but whose further progress may be hampered by the lack of a theoretical framework. In this regard, Markov random fields have yet to be proven practical and robust models in 3D.

 

In this paper, a new type of random field model is proposed that is based on a parameterization by means of the distance between any two outcome realizations of this random field model. The main idea is based on a simple duality between the covariance table calculated from a set of N_R realizations and the Euclidean distance between these realizations. Hence, instead of defining a random field in a very high-dimensional Cartesian space (dim = N), we define the random field in a much lower-dimensional and mathematically/computationally tractable metric space (dim max = N_R), since it is expected that the number of realizations is much less the number of grid-cells. The classical Karhunen-Loeve expansion of a Gaussian random field, based on the eigenvalue decomposition of an N x N covariance table, can now be formulated as function of the N_R x N_R Euclidean distance table. To achieve this, we construct an N_R x N_R kernel matrix using the classical radial basis function, which is function of the Euclidean distance and perform eigenvalue decomposition of this kernel matrix. The generalization to non-Gaussian random fields is easily achieved by using distances other than the Euclidean distance. In fact, the distance chosen can be tailored to the particular application of the random field model at hand.

 

It is shown how this modeling approach creates new avenues in spatial modeling, including the generation of a differentiable random field, a random field model for multiple-point simulations, the ability to easily update existing realizations with new data, a more realistic modeling of spatial uncertainty, and the fast and effective construction of prior model spaces for solving inverse problems.

 

G. Ayeni and B. Biondi

Stanford University

 

Time-lapse (4D) imaging of conventional reservoirs is a well-established technology. However, there has been little success of 4D applications in complex geology (e.g. sub-salt reservoirs) or in areas where repeatability is difficult, expensive or impossible (e.g. due to the development of facilities between surveys).

 

A regularized least-squares inversion of the linearized wave equation is proposed as a means for compensating for poor and uneven sub-surface illumination under complex overburden as well as image differences resulting from different acquisition geometries. This approach involves a joint deconvolution of migrated images from different vintages with explicitly computed filters derived from the Hessian of the linearized wave equation. By using such a formulation, we solve both the imaging and monitoring challenges as a single problem.

 

A more accurate image of the sub-surface and its evolution through time would be obtained without the need (and hence cost) for perfectly repeatable survey geometries. The realistic numerical experiments we have conducted so far indicate that the joint inversion technique appears to yield more accurate time-lapse results, and to be more robust with respect to errors in the forward-modeling operator, than the direct differencing of independently inverted images.

 

T.G. Kolda

Sandia National Laboratories

 

In this talk, I will describe Generating Set Search (GSS), a derivative-free optimization method that is an extension of pattern search. GSS is an iterative method that generates new trial points according to a search pattern at each iteration. These methods date back to Fermi and Metropolis and are often characterized as “slow but sure”; however, their robustness makes them worth a closer look - they are particularly well-suited to engineering optimization problems because they only require function evaluations and are largely immune to errors and break-downs in the simulations. Moreover, these methods are embarrassingly parallel, so it is possible to make them relatively fast by performing the function evaluations simultaneously.

 

A major focus of the GSS work at Sandia has been the development of APPSPACK (Asynchronous Parallel Pattern Search package), our implementation of an asynchronous version of the algorithm. Removing the synchronization barrier in the standard GSS algorithm makes much better use of parallel resources and leads to reduced runtime, even though the number of function evaluations can sometimes increase. Moreover, APPSPACK is designed to be easy-to-use, robust, and efficient (in that order). I will compare it to other derivative-free software packages in features and performance.

 

I will also describe the theory that underlies GSS and how it has been adapted to handle constraints. Linear constraints require that the search pattern is modified to appropriately conform to the nearby boundary. Nonlinear constraints are handled by exact and smoothed-exact penalty functions. We have implemented these methods in APPSPACK, and I will present results on standard test problems.

 

I will conclude by summarizing the benefits of GSS and its implementation in APPSPACK for real-world problems in optimization. If time permits, I will discuss our current work on hybridizing APPSPACK to be suitable for global optimization.

 

R. Serban

Lawrence Livermore National Laboratory (LLNL)

 

Adjoint-based techniques and variational analysis are powerful mathematical methodologies for tackling some computational problems that are essential to many science and engineering applications. The capability of constructing and solving appropriate adjoint models provides (1) an efficient way to evaluate perturbations to data and (2) relatively cheap derivative information for quantities of interest. The applications of these include forward and inverse sensitivity analysis, parameter identification and dynamic optimization, error estimation, model evaluation, etc.

 

We begin by introducing the underlying ideas for using adjoint models to evaluate derivatives of quantities of interest. We focus on problems described by ordinary differential (ODE) or differential-algebraic (DAE) equations and briefly discuss topics related to the derivation and analysis of the adjoint models as well as related implementation and software issues. We conclude with some applications of adjoint methods to (1) computation of derivatives in the context of optimization for robustness and (2) assessment of the quality of reduced-order models under perturbations.

 

S. Ahn

Stanford University

 

Pressure and flow rate data monitored from permanent downhole gauges are complex in the sense that the two signals might not change in reciprocal direction (as required due to reservoir physics). To study the interaction between these two (noisy) signals we will compute the optimal response function (pressure inferred from flow rate) by solving a sequence of convex optimization problems. We will then see how this methodology can be used to reduce the noise originally present in the signals obtained. The results of this procedure will be compared to those of an approach based on least squares. We will eventually end the presentation by discussing ongoing research issues.

 

R. Younis

Stanford University

 

Reservoir simulation equations are invariably nonlinear, and solving numerical approximations to them often requires the construction and evaluation of large sparse Jacobian matrices. Moreover, the possibility of phase-transitions dictates that the precise physics governing the process is not known at any point until runtime. That is, the sparse nonlinear systems may change in dimension, functional form, and degree, all depending on the current evaluation state.

 

Automatic Differentiation (AD) is an algorithmic technique to automatically encode the handful of rules defining differentiation. Numerous AD software packages are currently available, and they vary in the assumptions they make regarding the intended usage and the client's need for speed. I present a unique high-performance differentiable vector calculus framework and software library that can be used in numerical simulation where some aspects of the physics may be known at compile time, and others at runtime.

 

Of more specific relevance to optimization, I show how the library can be used to assemble adjoints with variable degrees of how much is to be pre-computed and cached versus re-computed. Ultimately, adjoint-based codes developed within the framework, would be smart enough to understand both the machine architecture and the current problem in order to evaluate adjoints with the minimal necessary runtime cost, automatically.

 

K. Sund

University of Stavanger

 

Over the past few years there has been an unprecedented wave of capital spending in the exploration and production industry. Still, the expectations for improved capital efficiency from Smart Fields (sometimes called Integrated Operations) and its promise of "faster and better decisions" have not materialized. Industry headlines are filled with notable examples of multi-year, multi-billion-dollar overruns. Indications show that leaders of oil and gas companies may be less satisfied with their overall performance than at any time in the history of industry.

 

In this presentation, the main focus is on inter-organizational relationships between operators and suppliers in the context of Smart Fields. We have surveyed one large operator, three large suppliers, and some small suppliers operating on the Norwegian Continental Shelf (NCS). The survey included a broad range of technical professionals at different management and business levels and included questions related to the collaborative relationship between operators and suppliers. In this work we present and discuss some of the results from the survey. We will discuss the disconnection between operators and suppliers related to contractual/incentive based contracts. Further, end results with use of incentive based contracts will be illustrated and possible improvements will be discussed.

 

Improving collaboration between operators and suppliers offers perhaps the greatest challenge and, we believe, the greatest potential in achieving the much anticipated value creation from Smart Fields/Integrated Operations. This paper contributes to this by identifying the key disconnects between operating companies and suppliers.

 

M. Cardoso

Stanford University

 

In this presentation I will talk about the current state of Reduced Order Modeling (ROM) applied to reservoir flow simulation. After a brief introduction I will focus on the implementation of ROM in Stanford's General Purpose Research Simulator (GPRS) and on the application of ROM to a 3-D reservoir with 60,000 grid blocks.

 

The most important points of my presentation are: a) flow scenario selection: just one simulation was needed to generate the reduced-order basis; b) examples showing the predictive capability of the reduced-order basis for different scenarios; c) three different options for the use of ROM: 1) Proper Orthogonal Decomposition (POD), 2) POD + clustering 3) POD + clustering + Missing Point Estimation (MPE); d) the number of unknowns of the model can be reduced from1 20,000 (pressure and saturation for all grid blocks) to 39; e) significant speed-up of the ROM-based model when comparedw ith the original one (GPRS).

 

Y. Ye

Stanford University

 

Given a convex polygon (in 2D) specified by its vertices, and other k points (hub/server) located on the polygon, we present a fast algorithm to partition the polygon into k subregions such that the following three properties hold: (i) the closure of each subregion is a convex polygon, (ii) each subregion contains exactly one of the k points, and (iii) all subregions have equal measurable "area".

 

We show its extension and applications in multi-depot vehicle routing, air traffic control zoning, client/server service load balancing, and possible applications in Smart Fields such as well location/placing.

 

S. Zarantonello

3DGeo Inc.

 

This presentation is on a DOE project that J. Harris and S. Zarantonello are leading. Their objective is a software platform for 4-D seismic surveillance of CO2 sequestration processes, which ties in naturally to history matching of reservoir simulation models using time-lapsed seismic imaging.

 

R. Bratvold

University of Stavanger

 

The battle against deterministic forecasts and financial valuations in the oil and gas industry appears to have been won, as evidenced by the plethora of papers, conference sessions, and forums focused on uncertainty quantification or profit prediction. In light of this focus on uncertainty quantification and forecasting, it seems appropriate to scrutinize its value. A natural question to ask is whether the focus on uncertainty modeling has improved decision making?

 

In this seminar, we present the findings of a survey of oil and gas professionals that addressed the following two questions: To what degree has uncertainty quantification improved in the oil and gas industry over the last five years? Has this improvement translated into improved decision making?

 

Uncertainty quantification is not an end unto itself; removing or even reducing uncertainty is not the goal. Rather, the objective is to make a good decision, which in many cases requires the assessment of the relevant uncertainties. The oil and gas industry seems to have lost sight of this goal in its good-faith effort to provide decision makers with a richer understanding of the possible outcomes flowing from major decisions. The industry implicitly believes that uncertainty is reduced simply by modeling it and that decision quality improves with more information. The increased computing power combined with increased sophistication in model building now permits the construction of very detailed models incorporating most relevant parameters and their dependencies. Yet, decision makers are still not sure what they should do. Rather than basing important decisions on a single number, they now find themselves buried under a mountain of probability distributions. Uncertainty quantification seems to have confused as much as it has enlightened and we are reminded of Peter Drucker's sage advice that:

 

“There is nothing as inefficient as very efficiently doing the wrong things.”

 

To counter this uncertainty induced confusion, we present a decision-focused uncertainty quantification framework, which we hope, in combination with our survey results, will aid in the innovation of better decision-making tools and methodologies.

 

M. Thiele

Streamsim Technologies/ Stanford University

 

A bread-and-butter field management task for many reservoir engineers is to set monthly or quarterly injection/production target rates in mature oil fields under water flood or some other type of secondary or tertiary displacement. In many cases, these fields are producing well above the 75% water cut mark, and thus increasing the efficiency even by a few percentages is key target of overall field management. Despite its importance, the management of such fields is surprisingly simplistic, with the final rate target generally set by some sort of "excel" spreadsheet put together by the production engineers. A more proactive management tool is clearly needed. One possibility is to use streamlines.

 

Streamline-based flow simulation is generally associated with "faster" simulations and therefore being a good proxy to use in any work flow that might require many forward simulations. However, an overlooked factor of streamline simulation is the novel data that is generated by streamlines, such as identifying injector-producer pairs and quantifying the reservoir volumes associated with the pairs. Armed with this data it is possible to "promote" more efficient injector/producer pairs and demote less efficient pairs. This can lead to a more successful field management strategy.

 

This talk will present work in this area. The intent is to raise awareness of streamline specific data and how it might be used within the Smart Fields consortium.

 

D. Echeverria Ciaurri

Stanford University

 

The Stanford Smart Fields Consortium is a multidisciplinary industrial affiliates program with participants from several departments from Stanford University. The research there involves the development and testing of optimization, risk assessment, decision-making and real-time monitoring and model calibration techniques, within the oil industry.

 

In this talk we will first present the Stanford Smart Fields Consortium and describe the main lines of research followed there. In the second part of this presentation we will go through the loop that represents the Smart Fields paradigm and illustrate some of the intermediate stages in this loop by projects carried out within the Smart Fields Consortium.