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.
In this work, we address the challenge of modelling a complex, carbonate reservoir, where the fractures network, connected throughout a complex fault framework, represents large part of both the storage and the flow capacity of the system. The asset is a giant, onshore field, developed since the 90's by primary depletion through several horizontal wells, targeting anomalous fluid columns. Different culminations are characterized by specific production drive mechanisms. The objective is to integrate an impressive amount of data into a digital model, suitable to understand fluid flow behavior and support decision. The field is challenging in every geological and dynamic feature. The reservoir complexity ranges from the intricate structural framework (several hundreds of reverse faults), to the puzzling fractures network at different scales, to the unclear role of the low-porosity rock matrix, to the heterogeneous distribution - both laterally and vertically - of fluid properties, related to different combinations of hydrocarbon and acid components. The workflow is based on the adoption of Volume Based Modelling (VBM) to account for seismic faults. Then, large-scale fractures are modelled using a blend of stochastic and deterministic Discrete Fracture Networks (DFNs), while background fractures (BGF) are characterized using a Continuous Fracture Modeling (CFM) formulation. A Dual Porosity - Dual Permeability (DPDK) approach is then implemented for reservoir simulation. The model is finally reconciled with the production data by iterating between geology and simulated dynamic response. The whole modeling and simulation workflow, from static to dynamic model definition, is developed relying on company's top-class computational resources. The DPDK formulation, where DFN is the second medium while the first medium consists of BGF and rock matrix, allows us to simulate the main production mechanism: large-scale discontinuities – DFN – are withdrawal first, and then fluid is recharged by smaller scale features. Besides, the history matching phase, together with accurate production and Pressure-Volume-Temperature (PVT) data analysis, sheds light on the extreme heterogeneity of the field. Petrophysical properties, storage and effective apertures of discontinuities are calibrated according to the production history, and integrated into a comprehensive understanding of the reservoir. Eventually, we reveal how a robust history matched model may be used as a powerful tool to understand the impact of all the involved criticalities on the subsurface fluid behavior and movement in a complex fractured carbonate setting. The challenges addressed in this work provide relevant best practices for carbonate reservoir modelling, in particular highlighting the role of the integration between geology and reservoir engineering to minimize subsurface uncertainties. Furthermore, the PVT model developed in this study proposes new migration scenarios to explain the sour gas distribution. Finally, optimized procedures to tackle numerical criticalities using advanced reservoir simulators are disclosed.
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 the economic value of a project. This methodology has already been successfully applied in the concept selection and concept definition phases of field development projects. Recently it has been further improved to be employed both in the evaluation stage, of a new discovery, and in the revamping of mature fields with production history. Introduction The Full Range Risk Analysis is aimed at investigating the whole range of possible reservoir development combinations. Its main objectives are:the support of the sanction of an economic discovery, considering an acceptable and accepted risk; the estimation of the value of information (VOI) of new data acquirement and the comparison between different appraisal strategies; the choice of the best development plan taking into account the main impacting reservoir uncertainties (mainly during concept selection phase);the quantification of downside/upside potential of the reservoir and of residual risk associated to field with production history. The presented workflow is a valuable tool in assessing the risk associated to a particular development scenario taking into account the entire surface and sub-surface uncertainties and therefore a methodology to support decisions in development projects. The FRRA methodology is fully based on Montecarlo approach and massive simulations of 3D reservoir models. Full Range Risk Analysis Workflow The current paper describes in detail the standard FRRA workflow along with the latest developments of the methodology, starting from the uncertainty definition phase up to the frequency distribution of the target variable, i.e. ultimate recovery, plateau length, NPV.
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