Summary In preparation for the SPE Applied Technology Workshop (ATW) held in Brugge in June 2008, a unique benchmark project was organized to test the combined use of waterflooding-optimization and history-matching methods in a closed-loop workflow. The benchmark was organized in the form of an interactive competition during the months preceding the ATW. The goal set for the exercise was to create a set of history-matched reservoir models and then to find an optimal waterflooding strategy for an oil field containing 20 producers and 10 injectors that can each be controlled by three inflow-control valves (ICVs). A synthetic data set was made available to the participants by TNO, consisting of well-log data, the structure of the reservoir, 10 years of production data, inverted time-lapse seismic data, and other information necessary for the exercise. The parameters to be estimated during the history match were permeability, porosity, and net-to gross- (NTG) thickness ratio. The optimized production strategy was tested on a synthetic truth model developed by TNO, which was also used to generate the production data and inverted time-lapse seismic. Because of time and practical constraints, a full closed-loop exercise was not possible; however, the participants could obtain the response to their production strategy after 10 years, update their models, and resubmit a revised production strategy for the final 10 years of production. In total, nine groups participated in the exercise. The spread of the net present value (NPV) obtained by the different participants is on the order of 10%. The highest result that was obtained is only 3% below the optimized case determined for the known truth field. Although not an objective of this exercise, it was shown that the increase in NPV as a result of having three control intervals per well instead of one was considerable (approximately 20%). The results also showed that the NPV achieved with the flooding strategy that was updated after additional production data became available was consistently higher than before the data became available.

Developing large E&P assets requires long-term commitments of capital that are tied to decisions on facilities, wells, scheduling, and production strategy. The decisions often must be made in the project-planning phase when large uncertainties exist that can lead to project risks. We present an optimization system and method that enables finding more-optimal reservoir-planning and managementdecision alternatives under conditions of uncertainty, such that the associated risks can be managed. The system integrates a global, stochastic search-optimization algorithm, finite-difference reservoir simulation, and economics. The optimization problem is posed with This is paper SPE 88991. Distinguished Author Series articles are general, descriptive representations that summarize the state of the art in an area of technology by describing recent developments for readers who are not specialists in the topics discussed. Written by individuals recognized as experts in the area, these articles provide key references to more definitive work and present specific details only to illustrate the technology. Purpose: to inform the general readership of recent advances in various areas of petroleum engineering.

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The presence of a large number of geologic uncertainties and limited well data typically increase the challenges associated with hydrocarbon recovery forecasting. Although recent advances in geologic modeling enable the automation of the model generation process by means of next-generation geostatistical tools, the computation of the reservoir dynamic response with full-physics reservoir simulation remains a computationally expensive task, which in practice requires considering only a few (but which?) of the many probable realizations. This paper presents a workflow that demonstrates the potential of capturing the inherent model uncertainty more accurately and assists in production-forecast business decisions. This workflow uses a history matching approach that directly interfaces the Earth modeling software with a forward simulator. It relies on the rapid characterization of the main features of the geologic uncertainty space, represented by an ensemble of sufficiently diverse history matched model realizations at the high-resolution geological scale. This workflow generates a more accurate result by obeying known geostatistics (variograms) and well constraints. We implement a multi-step, Bayesian Markov chain Monte Carlo inversion in which the proxy model is guided by streamline-based sensitivities. This process eliminates the need to run a forward simulation for each model realization, which significantly reduces the computation time. Efficient model parameterization and updates in the wave-number domain, based on discrete cosine transform (DCT), is used for fast characterization of the main features of the geologic uncertainty space, including structural framework, stratigraphic layering, facies distribution, and petrophysical properties. The application of the history matching workflow is demonstrated with a case study the combines the geological model with approximately 900K cells, four different depositional environments, and 30 wells with a 10-year waterflood history. Finally, the method is described to dynamically rank the reconciled model realizations to identify the highest potential of capturing bypassed oil and to optimize business decisions for implementing improved oil recovery (IOR). The main features include the following: Calculation of pattern-dissimilarity distances, which distinguish two individual model realization in terms of recovery response Deployment of very fast streamlined simulations to evaluate distances Application of pattern-recognition techniques to assign several realizations, representative for production forecasting, to full-physics simulation Derivation of the probability distribution of dynamic model responses (e.g., recovery factors) from the intelligently selected simulation runs

Optimal Improved Oil Recovery (IOR) depends significantly on the ability to estimate volumes and locations of bypassed oil from available historical data. Assisted simulation history-matching techniques are being used to estimate remaining reserves volumes and locations. This paper presents an approach to history match that more accurately captures model uncertainty. The novelty lies in direct interfacing between the geological modeling software and a forward simulator with the rapid generation of model updates in wave-number domain.We describe a Bayesian workflow based on two-step Markov chain Monte Carlo (MCMC) inversion. Arguably, the MCMC methods statistically provide the most rigorous way of sampling posterior distribution, but when deployed in direct simulation, suffer from high computational cost. We outline an approach where the proxy model is guided by streamline-based sensitivities, dispensing with the need to run forward simulation for every model realization. We generate an ensemble of sufficiently diverse static model realizations at the high-resolution geological scale that generates more accurate results by obeying known geostatistics (variograms) and well constraints. An efficient model parameterization and updating is described for rapid characterization of the main features of geologic uncertainty space: structural framework, stratigraphic layering, facies distribution, and petrophysical properties.We validate the workflow on a case study combining geological model with ~900k cells, four different depositional environments and 30 wells with 10-year water-flood history. The method can be used for successfully identifying the highest potential to capture bypassed oil and for implementing IOR. A history-match method is presented that has potential to lead to better IOR decisions and results through more accurate simulation models and inherent quantification of uncertainty.

Developing an exploitation plan for an oil field has always been challenging. This paper presents different approaches and addresses the important question of how to allocate asset resources to maximize profit. This study presents the analysis of integrated field case studies. Because of their complexities, integrated studies can offer valuable insights. Based on a field's heterogeneity and complexity, it can be relevant to divide it into different investment units. Individual studies can be generated, and based on their results, different exploitation strategies can be visualized. The strategies are based on economic criteria, such as net present value and the efficient use of resources. Capital investment efficiency is the key indicator that should direct resource allocation. Although there is no fixed method to find optimal decisions in a capital project, the FEL (front-end-loading) methodology measures and increases the level of project definition, thereby increasing the probability of project success at any stage of the life of the oil field. Traditionally, economic resources have been the principal metrics for analysis. However, the term resources encompasses many potential inputs to the system, e.g. drilling, facilities, enhanced recovery, and allocations. In complex oil fields with many independent reservoirs, it has been found that each reservoir has its own characteristics. That leaves room to experiment on new combinations for development, such as infill drilling, injection in the most favorable reservoirs or new potentional drilling locations. In many cases, it was found that the oil price had the greatest impact on projects, because their profit margin is lower than the rest of field. Small reservoirs or reservoirs that require marginal investment show the highest efficiency in investment, which means that investment efficiency is inversely related to the total amount of investment. This finding indicates that focusing solely on investment efficiency can mislead the optimal decision. Introduction An integrated field study is traditionally a sequential process; decisions are often broken down and disconnected. Often, reservoir engineers just model reservoir response to the bottom-hole, production engineers model the whole wellbore to the well-head, and process engineers model the surface facilities from the wellhead to the tank [Saputelli et al., 2002]. In general, most parties assume constant pressures at the boundaries throughout the simulation period. For the above reasons, project results often deviate from the project plan. In the building of field development plans, not all subsurface uncertainties are considered when evaluating all feasible surface scenarios. Changes in well productivity, water-front advance, free-gas production, and fluid composition will affect both reservoir and surface response. Because of these fluctuations, surface facilities may remain sub-utilized, a reservoir's full potential may not be obtained, and field economics may not reach peak performance. Field development decisions must be made despite uncertainties in well performance, subsurface response, equipment failure rate, and downstream demands. The heterogeneity of information and complexity of current hydrocarbon assets requires an iterative approach to identify the best opportunities. To succeed, this approach requires risk and uncertainty management. Optimal field development planning may involve identifying opportunities for increasing production from reservoirs, wells, and surface equipment, with the minimum effort.

The commercial exploitation of shale resources requires the design of hydraulic fracture programs to optimize the stimulated reservoir volume. Challenges include the engineering of staging, sequencing, injection volume, and proppant selection. This paper describes the development of a 3-D finite-element model to simulate growth of fracture complexes as functions of rock stress, brittleness properties, and injection conditions. The fracture complex is modeled as a major fracture, which consists of a cohesive crack and microcracks that form with variable continuum mechanical damage. Variations of permeability and porosity affect the continuum mechanical damage, which is used to investigate the fracturing sequence of three stages of injections in a horizontal well and generates three sets of fracture complexes. Inputs include a geomodel, the initial geostress, and pore pressure. Elastoplastic deformation coupled with both porous and gap flows are simulated. The model is also used to investigate the influence of rock brittleness on the fracture complexity. Material brittleness is the product of the elasticity modulus and Poisson’s ratio. Three sets of values of material brittleness were used in the calculation for comparison. The same loads and initial conditions of the first example were used in the model. The results include two sets of distributions of fracture propagation and damage evolution for two different injection sequences, pore pressure distribution after fluid injection stimulation, three sets of numerical results of distribution of fracture, and mechanical damage corresponding to different values of brittleness. Numerical results indicate that adjusting the sequence of multi-stage fracturing significantly influences the stimulated formation volume fractured. The model can be used to design more optimal fracture programs.

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