Eliana Luci Ligero

Decision-Making Process in Development of Offshore Petroleum Fields

Risk is inherent to all phases of a petroleum field lifetime due to geological, economic and technological uncertainties, which are very significant on oil recovery in development phase, the focus of this work. The acquisition of additional information of uncertain attributes and flexibility during the development are key points to risk mitigation. The Value of Information (VoI) is used to quantify the benefits of new information, giving more accuracy to the project. The Value of Flexibility (VoF) measures the benefits of adding flexibility to the project considering different possible scenarios. A new and reliable methodology has been proposed to quantify VoI and VoF based on the decision tree technique in order to combine the uncertain attributes. All reservoir models generated by the tree are submitted to parallel simulation and Geological Representative Models (GRM) are selected to represent geological uncertainties. The methodology includes the criteria used for selection of GRM, optimization of production strategies of each GRM considering the gathering of additional information and statistical treatment of the results. The methodology has been applied in a decision-making process of a giant offshore petroleum field. The field has been developed by blocks due to its physical limitations and intrinsic characteristics and the high investment necessary to develop a giant field. The contributions of this work are (1) to show the importance of VoI and VoF concepts in decision-making process in petroleum field development and the complexity of this type of decision, (2) to apply the proposed methodology in a giant offshore field modeled by parts, minimizing risks associated to the development of this type of field and (3) to evaluate the importance of the reservoir uncertainties in risk mitigation. An additional important contribution is to present the details of the use of reservoir simulation in the process, trying to obtain the best relationship between computation effort and reliability of the decision making process. Introduction All phases of a petroleum field are influenced by uncertainties. The uncertainties are, usually, associated to reservoir geological characteristics or economic and technological parameters. The geological uncertainties influence the economic results of the project; however they can be mitigated by acquisition of additional information. The economic uncertainties depend on the political, financial and economic scenarios of the E&P industry. Although, economic parameters, such as the oil price, can highly influence the project evaluation, they can’t be mitigated and have to be updated when they suffer significant variations. The technological parameters have influence mainly on production, investment and operational costs. The focus of this work is restricted to the reservoir geological uncertainties and consequently to flow characteristics. Considering offshore petroleum fields, the cost of additional information is high due to high investment and low flexibility. In such cases, the decision analyses process needs to be probabilistic, mainly when the production strategy is defined. Probabilistic methodologies have to be simplified since the process is complex; there are many possible decisions and the computational cost of the reservoir simulation, the tool employed to evaluate alternatives, is high. Each possible scenario is associated to probabilities, which are quantified trough risk analysis. The risk analysis can be applied to the various phases of the development process of a petroleum field (Santos and Schiozer, 2003). As decisions are different for each reservoir life phase, the methodologies and tools vary according to the phase. In exploration phase, the risk methodologies are well defined (Newendorp and Schuyler, 2000). In the transition from appraisal to the development phase, although the level of uncertainty is smaller, the importance of risk associated to the recovery factor may increase significantly. In this phase, various critical decisions, mainly related to the definition of the production strategy, have to be taken and the process complexity arises from high irreversible investments, large number of uncertainties, strong dependence of the results associated with the production strategy definition, and necessity of accurate reservoir behavior prediction (Schiozer et al., 2004). In this work, the decision-making process considers the uncertainty and risk associated to the geological and flow characteristics of a giant offshore field that is developed by modules. Some reasons to develop a giant field by modules are: its intrinsic characteristics, a strategy to reduce technical risks and budget and physical limitations. It is common

Quantifying the Impact of Grid Size, Upscaling, and Streamline Simulation in the Risk Analysis Applied to Petroleum Field Development

Geological uncertainties usually have a strong impact on the decisions applied to petroleum field development. Methodologies to quantify the impact of uncertainties are still not well established due to the amount of variables that have to be considered. The complete analysis usually depends on geological, economic and technological uncertainties that have different degrees of impact in the recovery process and may affect the decision process in different levels depending on the problem, reservoir characteristics, recovery mechanism and stage of field development. The objective of this paper is to evaluate the importance of the numerical simulation process in a risk analysis applied to a petroleum field development. The use of fast estimation of recovery factor can be used in the exploration risk analysis but, during appraisal and development phases, more reliable techniques must be used to predict the reservoir performance in order to quantify accurately the impact of uncertainties and evaluate risk. One of the drawbacks of the use of numerical simulations can be the required computational effort. Therefore, this paper evaluates the impact of using different degrees of accuracy in the procedure, calculating risk with different options of grid size, number of attributes, levels of uncertainties and also comparing the results with streamline simulation which is used also for very fine models. The proposed procedure is tested in the SPE 10th Comparative Solution and a comparison is presented for several different options. Finally some conclusions are taken based on accuracy, required computational effort, model scale, comparing also results from simulation through finite difference model and streamline. Introduction In the past, prediction of reservoir performance and field economic evaluation were normally carried out with one or a few possible models mainly due to restrictions in the computer resources. Nowadays, probabilistic forecasts are possible but due to the high number of uncertain variables and complexity of the models, simplifications are still necessary. Petroleum field development is always strongly related to uncertainties and risk. The complete economic analysis of the problem involves many uncertain parameters with different degrees of impact on the decisions that have to taken in the process. The most important uncertainties are related to geological models, economic parameters and technology. To evaluate the risk involved in the process, it is necessary to quantify the impact of these uncertainties, which is normally measured with objective functions. The analysis can be simplified if the uncertain parameters are combined in three groups: volumes in place (VHIP), recovery factor (RF) and economic model. Uncertainty related to volumes in place is a direct function of the geological characterization and it is normally reduced significantly during appraisal phase although it also may be responsible by high risk variations due to new and sometimes unexpected information. Considering the economic model, oil price is the major font of uncertainty. Investments and costs variations related to new technologies have also to be considered due to the long period of typical projects, especially in offshore fields. Regulatory issues also may affect in some cases. All parameters that affect reservoir behavior may be considered in the uncertainties related to recovery factors. In such cases, rock and fluid attributes, reservoir mechanism and production strategy are the most common parameters to be considered. These uncertainties are more important during appraisal and development phases when many important decisions related to production facilities are taken. One of the main goals of the risk evaluation is to identify the impact of each uncertainty in order to simplify the problem without significant accuracy reduction. The problem becomes more difficult because the impact of these uncertainties varies during the development of the fields. Most of the published works (Newendorp, 1975; Garb, 1988) related to risk measurement have the focus on the exploration phase where uncertainties due to reservoir performance prediction have small impact and probabilistic SPE 79677 Quantifying the Impact of Grid Size, Upscaling, and Streamline Simulation in the Risk Analysis Applied to Petroleum Field Development Eliana L. Ligero, SPE, UNICAMP; Célio Maschio, SPE, UNICAMP; Denis J. Schiozer, SPE, UNICAMP

Risk Assessment of Petroleum Fields—Use of Numerical Simulation and Proxy Models

Abstract The development of petroleum fields is a complex task due to the high influence of uncertainties on E&P projects. During the appraisal and development phases, uncertainties related to geologic and fluid models play an important role, especially in offshore heavy oil fields due to the low economic return, limited flexibility, and importance of reservoir modeling. The flexibility is limited because of the necessity to design the production facilities based on a low amount of information. The reservoir modeling process is important because risk of field development projects is normally caused by a high uncertainty on the recovery factor. Due to the necessity of a more robust evaluation of recovery factor, risk assessment methodologies normally are integrated with reservoir simulation, which is the best available tool to predict reservoir performance. However, higher precision on prediction of reservoir behavior is normally associated with fine simulation grid and high computation effort. In this article, some alternatives are presented to improve the efficiency of risk assessment, considering precision and computation effort. Among these alternatives are (1) use of coarse models, (2) use of coarse models modified to reproduce fine grid results, (3) simplifications on the risk assessment procedure, and (4) use of proxy models based on statistical (experimental) design and response surface methodology. A general discussion, including each alternative, use of upscaling techniques, reduction of grid size, number of attributes, use of parallel computing, and use of proxy models are made based on previous publications and results of a case study. The methodology applied to quantify risk involves a sensitivity analysis in order to reduce the number of critical attributes and simulation of reservoir models obtained through the combination of these attributes. Afterward, a statistic treatment is used to evaluate the risk involved in the process. Based on a case study, it is shown that the use of faster simulation models and proxies can speed up risk assessment, but a few steps must be performed to guarantee the quality of the results.

Risk assessment for reservoir development under uncertainty

Decision analysis applied to petroleum field development is always strongly related to risk due to the uncertainties present in the process. Methodologies to quantify the impact of uncertainties are still not well established due to the amount of variables that have to be considered. The complete analysis usually depends on geological, economical and technological uncertainties that have different degrees of impact in the recovery process and may affect the decision process at different levels depending on the problem, reservoir characteristics, recovery mechanism and stage of field development. This paper shows several details of a methodology that can be applied to complex and simple reservoirs in a reasonable amount of time, discussing especially the influence of the model used to predict recovery, choice of production strategies to be used in the process, number of attributes and type of information necessary to obtain reliable results. A discussion of data integration among geology, reservoir engineering and economic analysis also is presented in order to reduce the amount of information necessary and time for the process. Some results are presented to show the advantages of automation and parallel computing to reduce the total time of the procedure where reservoir simulation is necessary for reservoir performance prediction.

Improving the Performance of Risk Analysis Applied to Petroleum Field Development

The appraisal phase in a petroleum field is characterized by several uncertainties, high investment and critical decisions, which are always strongly related to risk. In the past, it was usual to realize production forecast based on a deterministic simulation model. However, production forecast obtained by a probabilistic approach allows the quantification of uncertainty in the reservoir performance by numeric flow simulation of several possible models. Current hardware permits to incorporate more accurate production prediction in the decision processes. A probabilistic approach requires the definition of a methodology. The objective of this work is to develop a methodology to improve the performance of the risk analysis process, trying to get the best accuracy with the lowest number of simulation runs, using an automated process and parallel computing to accelerate the process. The methodology is based on simulation of several flow models representing possible scenarios of the reservoir, through the combination of the uncertain attributes. As simplification, sensitivity analysis is made to reduce the number of uncertain attributes. The simulation models are built through the derivative tree using only the critical attributes. To reduce the simulation time, parallel computing is also applied. After simulation of the models, a statistic treatment is used to obtain the risk curve of the production forecasts and of the net present value. Representative models are selected to integrate the analysis with economic uncertainties. The methodology is applied in petroleum fields and the advantages of the automated process and the simplified procedure are discussed. Introduction Petroleum field development and management are strongly related to risk due to several uncertainties that have to be considered. The most important uncertainties are related to the geological model, economic conditions and technological developments. Typically, the impact of these uncertainties is quantified in terms of volumes in place, recovery factor and economic indicators (Ligero et al., 2003). Uncertainty on the economic conditions is always present in the petroleum industry. Usually, during the exploration phase, uncertainties related to the volumes in place have great impact. In the Appraisal and Development phases, as more information is obtained, the importance of the uncertainty on recovery factor becomes significant. This paper deals with the quantification on uncertainties in these phases, adding new information to the methodology presented by Loschiavo et al. (2000), Steagall and Schiozer (2001), Ligero et al. (2003), Santos and Schiozer (2003), and other papers described in these references to perform risk analysis applied to petroleum field development using numerical reservoir simulation. The methodology used here is the same methodology presented by Steagall and Schiozer (2001). Depending on the complexity of the problem, size of the reservoir and importance of the project, it is not possible to include all uncertain parameters in the analysis and simplifications are necessary to yield viability of the process. Automation of the process, parallel computing (Ligero and Schiozer, 2002), special treatment of the geological attributes (Costa and Schiozer, 2002), treatment of production strategy (Santos and Schiozer, 2003), and fast simulation models (Ligero et al., 2003) are possible simplifications that are briefly discussed in this paper. Other approaches can be used, as presented by Salomão and Grell (2001). All these authors have performed the risk analysis with a fixed economic model because the objectives were normally to quantify the impact of geological uncertainties in the decision making process related to field development. However, if economic condition changes, some additional considerations have to be included in the risk analysis. In this paper, it is used the concept of representative models to test the importance of this variation and some discussion is presented to propose a methodology to integrate this step with the risk methodology. It is also presented a discussion about data integration among geology, reservoir engineering and economic analysis in order to reduce the amount of information necessary and time of the process. Some results are presented to show the advantages of automation and parallel computing to reduce the total time of the procedure where reservoir simulation is necessary in the reservoir performance prediction. SPE 81162 Improving the Performance of Risk Analysis Applied to Petroleum Field Development Eliana L. Ligero, Ana Paula A. Costa, Denis J. Schiozer, SPE, UNICAMP.

History Matching Using Uncertainty Analysis

A example of history match is presented, for a reservoir which the original simulation model does not reflect the production behavior. The remaining uncertain attributes were evaluated through a dynamic procedure during the sensitivity analysis. The objective of this work is to present new approaches to improve history matches. The methodology consists on a dynamic sensitivity analysis based on simulation of models where uncertain attributes are tested and compared with a base model. The sensitivity analysis must be performed for each well, and include pressure evaluation. Techniques were developed to analyze reservoir performance, like differential pressure maps between zones. New approaches to assess connectivity between zones were used, to give alternate structural models to the sensitivity analysis. The simulated value of each parameter may be compared with the base model or with the observed data. The attributes are selected and combined in a derivative tree. Parameters with opposite influence may be combined in order to fit the observed data and obtain the history match. This methodology may be helpful for reservoirs which simulation model does not reflect the production data, and with uncertainties on reservoir characterization.

Effect of Grid Size in Risk Assessment of Petroleum Fields

It was shown recently that it is important to evaluate risk through probabilistic methodologies that involve a high number of simulation models because of the number of uncertain attributes. Geological modeling yields reservoir models that are represented through fine grids with millions of blocks. A probabilistic risk evaluation based on such grids would require a very high computational effort. Therefore, an upscaling procedure is necessary to reduce the grid size but it is difficult to select a grid size that could represent an adequate balance between precision of risk assessment and computational effort. The methodology applied to quantify risk involves a sensitivity analysis in order to reduce the number of critical attributes and the simulation of reservoir models obtained through all possible combinations of these attributes. After the simulation of the models, a statistic treatment is used to evaluate the risk involved in the process. Several procedures can be used to speedup the process; however the number of simulation runs may be very high. Upscaling of the simulation models can decrease significantly the computational effort and global time of the risk analysis process but it can also yield an inadequate risk assessment. In this paper the effect of the grid size on the process is evaluated. It was developed a methodology (1) to select an adequate grid size and (2) to speed up the risk analysis process. The choice of geological representative models from coarse grid risk evaluation can to be useful to represent the risk on fine model, avoiding the simulation of all fine models, yielding a significant speedup up of the process. Practical applications of upscaling in a probabilistic risk assessment, that use the concept of representative models selected to characterize geological uncertainties, are shown through calculations performed in a petroleum field represented by a fine grid simulation model with geological uncertainties. Introduction Risk analysis has been used successfully as a support to the decision making process in petroleum field exploration phase. In fact, most of the published works (Garb, 1988; Rose, 2001 and Newendorp and Shuyler, 2000) are related to risk assessment in the exploration phase. In such a phase, the uncertainties due to reservoir performance prediction have small impact and probabilistic recovery factor treatment combined with Monte Carlo techniques may be sufficient to obtain the required precision. However, risk assessment in development phase can be complex and as consequence it is not used frequently. The complexity is resulting mainly from the interdependence among uncertainties, oil recovery and production strategy and the excessive time consumption in the modeling process. In this phase, numerical reservoir simulators are normally required. Some recent works have presented methodologies based on simulation reservoirs to evaluate the risk associated to the petroleum development phase, such as the derivative tree methodology (Loschiavo et al., 2000; Steagall and Schiozer, 2001, Schiozer et al., 2004) and the methodologies combined to experimental design and response surface methods (Damsleth, 1992; Dejean, 1999 and Venkataraman, 2000). The great advantage of the derivative tree methodology proposed by Steagall and Schiozer (2001) is the idea of geological representative models (GRM models). GRM models are a few models with different characteristics of the objective functions capable of representing the geological uncertainty of the process. An application of the GRM models is to integrate the geological uncertainty with economical and technological uncertainty and with production strategy (Ligero et al. 2003; Schiozer et al., 2004). Even with hardware advances and software improvement, the use of numerical simulators during risk analysis has as disadvantages the computational effort and total time of the process. In order to speed up the process some procedures are used. For example, Ligero and Schiozer (2002) presented the benefits of the automation of the process and of the use of parallel computing, Costa and Schiozer (2003) showed some possible simplifications related to the treatment of attributes in the process, Subbey and Christie (2003) used faster simulation models such as streamline model, Santos and Schiozer (2003) considered the treatment of the production strategy and Ligero et al. (2003) also employed streamline model and fewer blocks in the simulation models. Other approaches can be used as presented by Salomão and Grell (2001).

Comparison of Methodologies To Evaluate the Risk of Petroleum Fields

A risk analysis process can be applied to several p h ses of a petroleum field. The methodologies required in the decisionmaking process depend on the level of uncertainties , which vary according to the field phase. This work is foc used on the development phase and the decision process is based on probabilistic procedure to represent all possible s c narios of the reservoir. The uncertain attributes can be combined through derivative tree or Monte Carlo technique. The risk can be evaluated by the Net Present Value, which depends o the reservoir performance. The reservoir production pre diction can be obtained through numerical simulation or res pon e surface. Most of works consider only the applicatio n of these procedures and comparisons of these techniques are not well evaluated. The goal of this work is to apply the al ternative combinations of these techniques to petroleum field s and compare them to determine the result reliabilities. The combination of Monte Carlo and numerical simula tion is not viable, in some cases, due to the great numb er of simulations. The attribute combination by derivativ e tree can be an alternative, but can also yield a high number of simulation runs. Alternatives to speedup the proces s are to reduce the number of attributes and their discretiz ation levels or substitute the conventional reservoir modeling b y faster ones, such as the response surface, which is suited to valuate the impact of uncertainty on production forecasts. The contributions of this work are to: (1) to deter mine if the response surface can substitute the reservoir s imulator, (2) to evaluate the capability of the response surface to substitute the reservoir simulator in order to obtain the risk curves, (3) to provide a guide to select an adequate combination o f techniques according to the desired precision and ( 4) to determine if it is possible to reduce the number of simulation runs, maintaining the precision. Introduction Risk is always associated to a petroleum field, wit h minor or major intensity, depending on its life phase. In de velopment phase, the number of uncertainties is high, affecti ng strongly the financial results and requiring high investment . Demirmen (2001) states the risk associate in development dec isionmaking process involves suboptimal development and opportunity loss. For this reason, the decision-mak ing process in such phase must be probabilistic. Probabilistic approaches are common in exploration phase (Newendorp and Schu yler, 2000 and Rose, 2001). In development phase, the imp ortance of uncertainties increases significantly, mainly on the recovery factor, however probabilistic methodologies are not used frequently to assess the risk (Schiozer et al, 2004). Most of works present methodologies to assess risk in development phase and they present only illustrativ e examples of their application. Comparison of the performance of different risk analysis methodologies is not common in the literature. For this reason, the main goal of this work is to compare risk assessment methodologies in developmen t phase. The first step of a risk methodology is to combine th geological uncertainties. Two possible manners are: Monte Carlo and Derivative Tree techniques, resulting in many reservoir geological models. The second step is to calculate the value of some specific objective functions, suc h as Net Present Value and Cumulative Oil Production. These values can be obtained through numerical simulation flow o r faster simulation models. In the last step, the risk curve s are built through a statistical treatment (Figure 1). Figure 1. Risk assessment in development phase. SPE 107736 Comparison of Methodologies to Evaluate the Risk of Petroleum Fields Eliana L. Ligero, SPE, Fernanda V. Alves Risso, SPE, and Denis J. Schiozer, SPE, UNICAMP Uncertain Attributes Monte Carlo Technique Derivative Tree Technique Combination Objective Function Calculations Numerical Flow Simulation Risk Curves Risk Curves Statistical Treatment Faster Simulation Models Numerical Flow Simulation Risk Curves Risk Curves Statistical Treatment Faster Simulation Models

Comparison of Techniques for Risk Analysis Applied to Petroleum-Field Development

This paper was selected for presentation by an SPE Program Committee following review of information contained in a proposal submitted by the author(s).

WAG-CO2 Light Oil Recovery from Deep Offshore Carbonate Reservoirs

Brazilian pre-salt reservoirs are constituted by carbonate rock and light oil with some CO2 and high solution gas ratio. A sustainable production of oil from pre-salt reservoirs requires a destination for the produced CO2 to mitigate its emission into the atmosphere. CO2 has been used to improve oil recovery when combined with water injection in the water-alternating-gas process (WAG). WAG-CO2 is an Enhanced Oil Recovery (EOR) method that modifies the fluid and rock-fluid properties. This injection process is associated to hysteresis of relative permeability and capillary pressure. Before implementation of the WAG injection in a field, the use of the reservoir simulation is required, a tool used to predict the oil recovery. A more rigorous way to simulate this process is by using a compositional reservoir simulator, given that an Equation of State (EOS) must be used to represent the pressure, volume, temperature (PVT) data that is different from the representation considered in conventional Black-Oil models. An EOS obtained from conventional PVT experiments and swelling tests must be employed to adequately represent the phase behavior resulting from the CO2 dissolution in the oil. Changes in relative permeability and capillary pressure resulting from hysteresis associated with the alternation between the injected fluids in the WAG process must be considered in the simulation model, avoiding a non-realistic oil recovery prediction. The impact of changes in oil properties and the hysteresis effect are considered in the prediction of WAG-CO2 oil recovery from a reservoir with petrophysical properties similar to a real carbonate reservoir constituted by light oil (about 8% molar of CO2). Reservoir simulation results give an indication of the expected oil recovery from a reservoir with pre-salt characteristics, enabling one to decide if the WAG-CO2 process is indicated for implementation in practice.