A Risk Analysis Approach: From Subsurface to Surface
Abstract: The quantitative assessment of the risk associated with the economic exploitation of a hydrocarbon field is mandatory to support, in an efficient and comprehensive way, the decision process guiding the development management system of an oil company. The Full Range Risk Analysis (FRRA) approach developed in Eni E&P Division allows the identification of uncertainties arising from each step of an integrated reservoir study, and the evaluation of their impact on recoverable reserves and on t… Show more
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Realistic reservoir models are essential for efficient field management and accurate forecasting of hydrocarbon production. Such models, based on the physical description of the reservoir, need to be calibrated or conditioned to historical production data. The process of incorporating dynamic data in the generation of reservoir models, known as history matching, is traditionally done by hand and is a very tedious, time-consuming procedure that, in addition, returns only one single matched model. It has been shown that the best matched model may well not be a good predictor of future performance. In this work, one of the first field applications of the Neighbourhood Algorithm (NA) is presented. The NA is a stochastic sampling algorithm that explores the parameter space, finds an acceptable ensemble of data fitting models and extracts robust information from this ensemble in a Bayesian framework. The aim is to forecast hydrocarbon production accurately and to assess the related uncertainty by means of multiple reservoir models. The NA methodology was extensively applied to an offshore gas field and compared to a previously manually matched model. The Mistral field has been producing for 6 years from 7 wells. Gas and water productions and pressure data were available and the uncertainty quantification was consistently obtained. Algorithm control parameters and objective function definition effects were investigated. The posterior probability density functions of each unknown parameter, calculated taking into account the observed production data, were evaluated. The hydrocarbon production was forecast using Bayesian inference and the economic risk estimated. The overall process was carried out with a significant time reduction compared with the previous manual approach. The results presented suggest that use of stochastic sampling techniques in a Bayesian framework may well be a valid alternative methodology to the traditional industry workflow for the uncertainty quantification in producing fields. Introduction History matching is a very complex non-linear and ill-posed problem. Like most inverse problems, it is characterised by non-uniqueness of solution [24]. For this reason different combinations of the model parameters may lead to acceptable representations of the history of the reservoir. Traditionally, history matching is done by hand and is a very tedious, time-consuming procedure where the reservoir parameters are varied until a satisfactory match is obtained. In addition, this standard practice leads only to a single production forecast making unfeasible any assessment of uncertainty. Recently, thanks to increasing computer power and technology, computer-aided history matching techniques are becoming gradually more adopted by the oil industry. This is due to the great time-saving benefits they can offer over conventional trial-and-error approaches [25]. "Automatic" and "Assisted history match" techniques automatically vary reservoir parameters until a defined stopping criteria is achieved. In literature they can be divided into three main groups:Deterministic methods;Stochastic methods;Hybrid methods. During the last decades the application of stochastic methods has spread over all the disciplines of the oil industry [12, 13, 14, 15, 20]. However, even if the nature of the history matching problem has been widely recognized, the majority of the approaches adopted return only one matched model that eventually will be used to forecast production. Alternative solutions (i.e. other acceptable models) are usually not sought because of computational and human time constraints. However, neglecting the non-uniqueness of the inverse problem and selecting only one reservoir model could lead to errors in the prediction of the production as cleverly highlighted in the work of Tavassoli et al. [25]. In addition only one model does not allow an assessment of uncertainty in prediction.
Realistic reservoir models are essential for efficient field management and accurate forecasting of hydrocarbon production. Such models, based on the physical description of the reservoir, need to be calibrated or conditioned to historical production data. The process of incorporating dynamic data in the generation of reservoir models, known as history matching, is traditionally done by hand and is a very tedious, time-consuming procedure that, in addition, returns only one single matched model. It has been shown that the best matched model may well not be a good predictor of future performance. In this work, one of the first field applications of the Neighbourhood Algorithm (NA) is presented. The NA is a stochastic sampling algorithm that explores the parameter space, finds an acceptable ensemble of data fitting models and extracts robust information from this ensemble in a Bayesian framework. The aim is to forecast hydrocarbon production accurately and to assess the related uncertainty by means of multiple reservoir models. The NA methodology was extensively applied to an offshore gas field and compared to a previously manually matched model. The Mistral field has been producing for 6 years from 7 wells. Gas and water productions and pressure data were available and the uncertainty quantification was consistently obtained. Algorithm control parameters and objective function definition effects were investigated. The posterior probability density functions of each unknown parameter, calculated taking into account the observed production data, were evaluated. The hydrocarbon production was forecast using Bayesian inference and the economic risk estimated. The overall process was carried out with a significant time reduction compared with the previous manual approach. The results presented suggest that use of stochastic sampling techniques in a Bayesian framework may well be a valid alternative methodology to the traditional industry workflow for the uncertainty quantification in producing fields. Introduction History matching is a very complex non-linear and ill-posed problem. Like most inverse problems, it is characterised by non-uniqueness of solution [24]. For this reason different combinations of the model parameters may lead to acceptable representations of the history of the reservoir. Traditionally, history matching is done by hand and is a very tedious, time-consuming procedure where the reservoir parameters are varied until a satisfactory match is obtained. In addition, this standard practice leads only to a single production forecast making unfeasible any assessment of uncertainty. Recently, thanks to increasing computer power and technology, computer-aided history matching techniques are becoming gradually more adopted by the oil industry. This is due to the great time-saving benefits they can offer over conventional trial-and-error approaches [25]. "Automatic" and "Assisted history match" techniques automatically vary reservoir parameters until a defined stopping criteria is achieved. In literature they can be divided into three main groups:Deterministic methods;Stochastic methods;Hybrid methods. During the last decades the application of stochastic methods has spread over all the disciplines of the oil industry [12, 13, 14, 15, 20]. However, even if the nature of the history matching problem has been widely recognized, the majority of the approaches adopted return only one matched model that eventually will be used to forecast production. Alternative solutions (i.e. other acceptable models) are usually not sought because of computational and human time constraints. However, neglecting the non-uniqueness of the inverse problem and selecting only one reservoir model could lead to errors in the prediction of the production as cleverly highlighted in the work of Tavassoli et al. [25]. In addition only one model does not allow an assessment of uncertainty in prediction.
The studied reservoir consists of turbiditic slope channels which form elongated sandy bodies with dimension of 4 to 12 Km in length and 1 to 4 Km in width. The thickness is varying from few metres to almost one hundred. These channels are hydraulically separated and characterized by high values of porosity and permeability. These sand bodies were identified and porosity characterized by means of seismic data. The first three wells have proved the correlation between gas-bearing sands and "low-impedance" seismic responses. Nevertheless, since those wells did not cross all the sand bodies (only 6 out of 14) the main problem at that stage was the risk assessment of the hydrocarbon occurrence in the remaining bodies. In order to assess this risk the first approach was to identify, for all the bodies, relevant seismic criteria (Amplitude strength, Signal noisiness, Fragmentation and Recon AVO) to use for a qualitative evaluation defining a ranking of the undrilled bodies. The following step was to apply a Bayesian approach to assess the relevant probability of hydrocarbon occurrence. This technique requires the estimation of two parameters: prior probability based on geological area knowledge and likelihood derived from seismic indicators. On the base of the probability ranking a limited number of scenarios was defined. For each scenario a dedicated plan of development was assigned. A new appraisal well was drilled after this evaluation. This allowed verifying the validity of the ranking system used to estimate probability of success with regards to the undrilled segments. The results show good agreement between seismic data and well's results with one (positive) exception. Moreover a traditional "Risk Analysis" was performed in perforated bodies to sample all the critical parameters (static and dynamic) and to build several models. By these models a set of volumetrics and production profiles were obtained.
Simulation models for large complex reservoirs with a long production history are traditionally used in the framework of deterministic forecasts. The estimation of prediction uncertainties based on reservoir models with long run times is often impractical due to limited statistical data generated from direct full field reservoir simulation runs. Here, we use surrogate models to capture key performance indicators as a function of reservoir uncertainties. Monte Carlo sampling processes are applied for generating key parameter distributions and to identify representative simulation models for field development planning. Transparent workflow steps and a thorough validation exercise of the predictability of surrogate models is a prerequisite for obtaining prediction uncertainties based on proxy modeling results. In this work we present a case study for estimating prediction uncertainties including history data of a large mature offshore oil field. A workflow is designed for a limited number of simulation runs which is expected to affect the stability of statistical reservoir performance indicators. Proxy models are introduced in that framework for analyzing sensitivities and to prepare a basis for extensive data sampling. Alternative proxy modeling techniques are used to cross validate results. To assure an acceptable quality of the history match simulated field oil production rate and gas oil ratio as well as water cut and shut-in pressures in several wells are compared to their historical data. A quantitative measure for the simulation error is calculated for each case. Filtering techniques are applied for discriminating cases with a poor match quality. Sensitivities and correlation effects between field wide uncertainties and reservoir performance indicators are calculated. Representative full field simulation models representing P10, P50 and P90 for oil reserves are identified. The uncertainty quantification workflow was validated for a mature field case study and serves as a basis for field development planning scenarios.
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