A statistical screening methodology is presented to address uncertainty related to main geological assumptions in green field modeling. The goals are the identification of the entire range of uncertainty on production, learning which are the most impacting geological uncertain inputs and understanding the relationships between geological scenarios and classes of dynamic behavior.
The paper presents the methodology and an example application to a green field case study. The method is applied on an ensemble of reservoir models created by combining geological parameters across their range of uncertainty. The ensemble of models is then simulated with a selected development strategy and dynamic responses are grouped in classes of outcome through clustering algorithms. Ensemble responses are visualized on a multidimensional stacking plot, as a function of the geological input, and the most influential parameters are identified by axes sorting on the plot. Geological scenarios are then classified on dynamic responses through classification tree algorithms. Finally, a representative set of models is selected from the geological scenarios.
The example study application shows a final oil recovery uncertainty range of 4:1, which is reasonable for a green field in lack of data. Such high range of uncertainty could hardly be found by common risk assessment based on fixed geological assumptions, which often tend to underestimate uncertainty on forecasts. Ensemble outcomes are grouped in four classes by oil recovery, plateau strength, produced water, and breakthrough time. The adoption of such clustering features gives a broad understanding of the reservoir dynamic response. The most influential geological inputs among the examined structural and sedimentological parameters in the example application result to be the fault orientation and channel fraction. This screening result highlights the main drivers of geological uncertainty and is useful for the following scenario classification phase. Classification of the geological scenarios leads to five classes of geological parameter sets, each linked to a main class of dynamic behavior, and finally to five representative models. These five models constitute an effective sampling of the geological uncertainty space which also captures the different types of dynamic response.
This paper will contribute to widen the engineering experience on the use of machine learning for risk analysis by presenting an application on a real field case study to explore the relationship between geological uncertainty and reservoir dynamic behavior.
The knowledge of reservoir fluids phase behavior has always played an important role in oilfield development planning, reserves evaluation and screening of the potential for enhanced oil recovery. Nowadays operators aim more and more at fast-track development of discovered resources, therefore, any anticipation of thermodynamic properties is a business challenge: looking for "PVT-analogues" is the solution proposed in this paper. What adversely impacts massive scouting of PVT data usually is the limit of a small amount of readily available information, also due to the intrinsic complexity of the datasets and of the variety of output formats produced by different laboratories all over the world and over the years. In Eni a new tool for data mining based on the reorganization and thorough digitalization of the PVT archive is in advanced development. Standardization of the laboratory outcomes by templates, automatic loading into a corporate repository, in-house development of software tools for quality control, data mining and advanced statistical analyses, easy access through a properly designed interface: each of these steps is integrated in an upgraded data-driven approach to fluid properties prediction allowing an earlier understanding of the reservoir fluid system.
Siena, Martina (Politecnico di Milano) | Guadagnini, Alberto (Politecnico di Milano) | Della Rossa, Ernesto (eni S.p.A.) | Lamberti, Andrea (eni S.p.A.) | Masserano, Franco (eni S.p.A.) | Rotondi, Marco (eni S.p.A.)
We present and test a new screening methodology to discriminate among alternative and competing enhanced-oil-recovery (EOR) techniques to be considered for a given reservoir. Our work is motivated by the observation that, even if a considerable variety of EOR techniques was successfully applied to extend oilfield production and lifetime, an EOR project requires extensive laboratory and pilot tests before fieldwide implementation and preliminary assessment of EOR potential in a reservoir is critical in the decision-making process. Because similar EOR techniques may be successful in fields sharing some global features, as basic discrimination criteria, we consider fluid (density and viscosity) and reservoir-formation (porosity, permeability, depth, and temperature) properties. Our approach is observation-driven and grounded on an exhaustive database that we compiled after considering worldwide EOR field experiences. A preliminary reduction of the dimensionality of the parameter space over which EOR projects are classified is accomplished through principal-component analysis (PCA). A screening of target analogs is then obtained by classification of documented EOR projects through a Bayesianclustering algorithm. Considering the cluster that includes the EOR field under evaluation, an intercluster refinement is then accomplished by ordering cluster components on the basis of a weighted Euclidean distance from the target field in the (multidimensional) parameter space. Distinctive features of our methodology are that (a) all screening analyses are performed on the database projected onto the space of principal components (PCs) and (b) the fraction of variance associated with each PC is taken as weight of the Euclidean distance that we determine. As a test bed, we apply our approach on three fields operated by Eni. These include light-, medium-, and heavy-oil reservoirs, where gas, chemical, and thermal EOR projects were, respectively, proposed. Our results are (a) conducive to the compilation of a broad and extensively usable database of EOR settings and (b) consistent with the field observations related to the three tested and already planned/implemented EOR methodologies, thus demonstrating the effectiveness of our approach.
Fonseca, Rahul (Delft University of Technology) | Leeuwenburgh, Olwijn (TNO) | Della Rossa, Ernesto (Eni) | Van den Hof, Paul M. J. (Eindhoven University of Technology) | Jansen, Jan-Dirk (Delft University of Technology)
We consider robust ensemble-based (EnOpt) multiobjective production optimization of on/off inflow-control devices (ICDs) for a sector model inspired by a real-field case. The use of on/off valves as optimization variables leads to a discrete control problem. We propose a reparameterization of such discrete controls in terms of switching times (i.e., we optimize the time at which a particular valve is either open or closed). This transforms the discrete control problem into a continuous control problem that can be efficiently handled with the EnOpt method. In addition, this leads to a significant reduction in the number of controls that is expected to be beneficial for gradient quality when using approximate gradients. We consider an ensemble of sector models where the uncertainty is described by different permeability, porosity, net/gross ratios, and initial water-saturation fields. The controls are the ICD settings over time in the three horizontal injection wells, with approximately 15 ICDs per well. Different optimized strategies resulting from different initial strategies were compared. We achieved a mean 4.2% increase in expected net present value (NPV) at a 10% discount rate compared with a traditional pressure- maintenance strategy. Next, we performed a sequential biobjective optimization and achieved an increase of 9.2% in the secondary objective (25% discounted NPV to emphasize short term production gains) for a minimal decrease of 1% in the primary objective (0% discounted NPV to emphasize long-term recovery gains), as averaged over the 100 geological realizations. The work flow was repeated for alternative numbers of ICDs, showing that having fewer control options lowers the expected value for this particular case. The results demonstrate that ensemble-based optimization work flows are able to produce improved robust recovery strategies for realistic field-sector models against acceptable computational cost.
Reservoir structural modelling is one of the fundamental steps in a reservoir study workflow. The impact of the structural uncertainties on the dynamic response of the reservoir is well known and not negligible, but often the reservoir shape is considered as fixed due to the complexity to manage alternative geological structures in multi-realisations simulation loop. Nevertheless, both Risk Analysis (RA) and History Matching (HM) workflows strongly require a practical and time-effective methodology for structure management with an efficient uncertain geometry parameterization. In this work, an innovative methodology for structural uncertainty handling is presented. The methodology is based on the combination of Principal Component Analysis (PCA) and Elastic Gridding. In particular, the PCA-based parameterization is able to efficiently handle the geophysical uncertainty model, consistent with the geostatistical characterization as well. Such methodology has been structured in an internally developed tool. This tool is specifically designed for a direct handling of corner point geometry grids and allows changes of surfaces, shape and size of internal reservoir layers, fault throw and fault position and even new fault placement, honouring geological constraints. One of the key points of the proposed methodology is the integration of a geologically-oriented parameterization and a statistical parameters reduction technique (the above mentioned PCA) in a workflow which includes commercial HM/RA tool and a dynamic simulator. The result is an efficient structural uncertainty management framework suitable for Risk Analysis and History Matching studies. Among the field applications performed so far, two cases have been chosen aiming at showing the potentialities of the proposed approach. The first example is a history matching exercise on an undersatured oil reservoir. A comparison between the traditional and the “structural”, even if simplified, HM is herein provided, showing the improvement due to a better geologically-oriented uncertainty model. The second example is a risk analysis application on an oil field, with a strong uncertainty of the oil in place due to lack of accurate knowledge of the reservoir flanks shape. The application highlights the advantages deriving from the geophysical PCA-based workflow.
The dynamical impact of Structural Uncertainty is well known and not negligible, but often it is not considered in history matching because of its very complex management within the dynamical models.
Indeed, the reservoir geometry is typically kept fixed, and the history matching workflow is usually implemented with a single deterministic reservoir structure. This approach mainly arises from the sequence of operations performed in reservoir structural modelling (interpretation of seismic data, building of the 3D frame, population of the grid with petrophysical properties, upscaling, etc.) which makes very difficult a continuous update of the structural modelling in the framework of an optimization loop. Moreover, the commercial availability of integrated modelling tool is very limited.
The “big loop” approach is a methodology suggested by some authors (see for example Hanea et al., 2013) which aims at building an integrated reservoir modelling chain (from Geophysics and Geology to Reservoir Engineering) that is automated, consistent and updateable. In this way, all the steps of reservoir modelling (from Surface and Fault modelling to Geomodelling, from reservoir simulation to ensemble methods) are unified within a single automatic workflow which requires a strong multidisciplinary collaboration among engineers and geoscientists.
This approach seems to be the way forward, but it involves an all-embracing software chain that is still not available in standard software packages.
To overcome this issue, a more straightforward approach is presented in this paper, based on the combination of elastic gridding and an innovative parameterization of the structural uncertainty.
Caselgrandi, Ernesto (ENI E&P) | Cavanna, Giorgio Rocco (ENI E&P) | Corti, Elisa (ENI E&P) | Della Rossa, Ernesto (ENI E&P) | Rovellini, Marco (ENI E&P) | Suhardiman, Yohan (ENI Indonesia) | Cerutti, Andres Enrique (ENI Indonesia) | Sugama, Candra