The field development phase prior to investment sanction is characterized by relatively large uncertainties at the time important decisions have to be made. It is, for instance, crucial to select an appropriate recovery strategy (depletion or injection) to obtain optimal hydrocarbon cumulative production whilst ensuring good profitability of the project. Evaluation of reservoir as well as economic uncertainties and quantification of their impact are needed before the field development concept selection.
This paper describes how to stochastically assess reservoir and economic uncertainties and the screening process used to select the best recovery strategy. The chosen methodology is the combination of uncertainty studies, including both continuous, discrete and controllable parameters. The different screened scenarios are combined in a stochastic decision tree, built-up through decision and chance nodes, to establish a distribution of recoverable volumes and rank the recovery strategies given a chosen criterion. A second uncertainty study is performed by adding economic uncertainties to the initial set of reservoir uncertain parameters. Eventually a new decision tree is established and scenarios ranked using economic criteria.
The application of this methodology to an oil field from the Norwegian continental shelf and how recovery strategies are ranked are presented in this paper. The described methodology has exhibited the risks and uncertainties carried by the project, as it was possible to rank the different solutions based on the dispersion of the recoverable volumes distribution and/or on the net present value (NPV). In the context of a marginal or large capex project, a robust P90 case is required and this may therefore influence the choice of the recovery strategy. For instance, a scenario yielding the largest hydrocarbon volume may not be selected because it requires too many wells and/or too large investment if one of these criteria is defined as the most important. In addition, the combination of uncertainty studies enabled a full economic evaluation covering the entire recoverable volumes distribution whereas in many projects economic evaluation is focused on the P90, Mean and P10 scenarios.
The two-step integrated approach allows a decision to be made whilst taking into account both reservoir and economic aspects. Having a combined stochastic approach to the reservoir and economic uncertainties avoids a biased decision. All cases are stochastically covered and screened using a systematic and unified methodology that gives the same weight to each scenario.
Reliable forecasts of pressure, gas saturation and water production are of primary importance for the performance optimization of underground gas storages (UGS). This paper presents the development of a new methodology to achieve the history matching of radial reservoir models taking into account subsurface uncertainties.
Radial models are a simple but consistent way to modelize an aquifer underground gas reservoir, assumed single layered, with about few tens of cells in general to model explicitly the storage and the aquifer. Each reservoir cell holds porosity, permeability, thickness and depth properties. STORENGY has developed a specific radial reservoir simulator called PREPRE dedicated to pressure, gas column thickness and water predictions. Water coning options and Turner parameters (well water loading) have been accounted for semi-analtically, in order to make more reliable water predictions. A major advantage of such models is to run in a few seconds which fits with optimization process requirements when performing the history matching. In the standard workflow, we need to optimize hundreds of reservoir parameters in addition to the dynamics parameters like relative permeability / capillary pressure curves, residual saturation, etc.. This large number of parameters usually results in a very challenging and time-consuming optimization process.
We developed a methodology based on multi-parameters organized classification, based on the Kohonen method (Self Organizing Map algorithm). Models are built using maps of geological property classified in a matrix of about a hundred of classes. Using this approach, it is only needed to optimize the position of the model into the matrix with two parameters in addition to the dynamic parameters. This method then reduces the number of optimized parameters from a hundred to ten.
The objective function to be optimized contains two or three sub-objective functions like well pressure, gas column thickness and water production history-matching errors. We can combine all objectives into one in order to perform mono-objective optimization, but the best approach is to perform multi-objective optimization for uncertainty management purposes. The result is a population of models lying on the pareto front, thus providing information on the models uncertainty.
The topology of the objective functions is very complex, irregular and contains a lot of local minima. An efficient optimizer capable of reaching the global minimum with a good probability is needed. For that purpose, genetic-based CMA-ES optimizer was chosen. Although it converges slowly and needs a lot of function evaluations, usually it always finds a better minimum, compared with other standard optimizers. A new multi-objective CMA-ES version has also been developed, combining elitist and non-elitist methods to get the best compromise between speed and performance, in order to better describe the global pareto front.
These workflows are tested through several optimization processes conducted on various natural gas storage assets. The results illustrate the added value of such approach, particularly the quality of the history matching and the possibility to assess the uncertainties using several matched models for more reliable exploitation forecasts.
This paper shows the application of two ensemble-based assimilation methods, the Ensemble Kalman filter (EnKF) and the Ensemble Smoother (ES), to constrain an underground gas storage site to well pressure data. The EnKF is a sequential data assimilation method that provides an ensemble of models constrained to dynamic data. It entails a two-step process applied any time data are collected. First, production responses are computed for every model within the ensemble until the following acquisition time. Second, models are updated using the Kalman filter to reproduce the data measured at that time. The EnKF has been widely applied in petroleum industry. More recently, the ES was successfully applied to a real field case. This method is also based on the Kalman filter, but the update is performed globally over the entire history-matching period: values simulated at assimilation times are considered all together in the update step. The uncertain parameters considered here are the porosity and horizontal permeability values populating several layers of the geological model. Applying both the ES and EnKF methods, the spread within the ensembles is reduced and the predictions based on the ensembles of updated petrophysical distributions get closer to the pressure data corresponding to the history-matching and prediction periods.
GDF SUEZ and Sonatrach will develop in partnership the main fields of the prolific Sbaa basin, SW Algeria. In this basin, the main gas levels comprise the Cambrian and Upper-Ordovician reservoirs, sealed and sourced by the Silurian "Hot Shales?? Formation. Average CO2 content of raw gas is in excess with regard to sales gas specifications. As a result, CO2 removal is required as part of gas treatment and it is intended to install an amine unit in the future Central Processing Plant for such purpose. Regeneration of the amine results in the release of significant quantities of CO2, which amounts to ca 1200 t/day. Looking for a solution to re-inject underground this CO2 was decided, within the project exploitation perimeter. Such an initiative has materialized with the achievement of a screening and feasibility study during the development conceptual phase, which identified the two best structures for underground CO2 sequestration. In both cases, CO2 should be re-injected in the aquifer, in the water leg, away from gas production area. A pilot well will be drilled to address the geological and reservoir uncertainties, so as to validate the concept.
For now, preparatory sensitivity studies, using Cougar® software (developed and marketed by IFP) were performed to identify key parameters controlling the CO2 re-injection performance. These probabilistic results show that, if effective permeability and reservoir heterogeneity are from far the main drivers, the mobility of fluid in place, such as gas or formation water, strongly controls the performance, ranging from 55 to 98 % of CO2 to be injected. Based on experimental design type of approach, this study enable to rank the main uncertain parameters (such as type of wells, number of injectors and maximum operating pressure) and to quantify their respective contribution to the underground CO2 storage performance.
This type of study helps to determine the priority in the data acquisition program of the future pilot well and to set-up the basis of design of the CO2 sequestration facility.
Geological sequestration of CO2 is one of the foreseen solutions envisaged to reduce significantly the atmospheric concentration of this greenhouse effect gas. CCS projects (CO2 capture and sequestration) propose a challenging approach that could lead to a major first step in reducing anthropogenic emissions of CO2. Like some other companies with several ongoing projects, GDF SUEZ is one of the leaders to develop such technology in a near future (Mulders et al, 2008 and Saysset et al., 2006).
The context of this CCS project in Algeria is the removal of the CO2 in excess, from the raw hydrocarbon produced and its injection and retention within the water leg of a producing gas field. The total amount of CO2 to be re-injected corresponds to 3.60 Gsm3 approximately over the whole project duration. Even if efforts have been made to develop this technology for several years, modellization of geological, chemical and physical processes at stake is still the subject of R&D investigations. Main issues to be addressed are various, dealing with well injectivity, performance sustainability, well and storage integrity, short to long term monitoring. The most suitable solutions have to be selected and adapted to the specificity of the project, long in advance. In the G&G domain, sensitivity studies are required to identify key parameters on CO2 storage performance and improve the level of prediction. A screening study was first performed to identify the best candidates for CO2 re-injection among all candidates' gas fields. The 6 main criteria chosen to rank the fields were: -storage capacity - cap rocks quality - reservoir injectivity - type of fluid originally in place - reservoir depth - distance to the treatment surface facility. At the end of the process, a small field, 4km² area, but with an important vertical relief (300m), was selected (Figure 1). The main reservoir presents high petrophysical properties with porosities and permeabilities up to 20% and 1000 mD respectively, within the Cambro-Ordovician sands. The choice was made to re-inject the CO2 within the water leg in order to not interfere with the gas production at top of the field. However, reservoir parameters of the dynamic model are not fully characterized in this part of the field, far away from any existing wells, and then it makes necessary to cope with a lot of uncertainties.
One of the main concerns in the oil and gas business is generating reliable reservoir hydrodynamics forecasts. Such profiles are the cornerstones of optimal technico-economical management decisions. A workflow combining different methods to integrate and reduce most of the subsurface uncertainties using multiple history matched models (explaining the past) to infer reasonably reliable forecasts is proposed.
A sensitivity study is first performed using experimental design to scan the whole range of static and dynamic uncertainty parameters using a proxy model of the fluid-flow simulator. Only the most sensitive ones with respect to an objective function (OF) (quantifying the mismatch between the simulation results and the observations) are retained for subsequent steps.
Assisted history-matching tools are then used to obtain multiple history-matched models.
To obtain probabilistic pressure profiles, multiple history-matched models are combined with the uncertain parameters not retained in the sensitivity study, using the joint modeling method.
Another way to constrain uncertain parameters with observation data is to use Bayesian framework where a posteriori distributions of the input parameters are derived from the a priori distributions and the likelihood function. The latter is computed through a nonlinear proxy model using experimental design, kriging, and dynamic training techniques.
These two workflows have been applied to a real gas storage case submitted to significant seasonal pressure variations. The obtained probabilistic operational pressure profiles for a given period are then compared to the actual gas storage dynamic behavior so that we can compare the two approaches and assess the added value of both proposed workflows.
History matching is an integral part of reservoir production forecasting, risk analysis and uncertainties quantification workflows. One has to cope with the non-uniqueness issue as history matching is an ill-posed inverse problem, due to a lack in constraints and data. Dealing with several history matched models is therefore critical and assisted history matching tools are of great interest to speed up the process.
In practise, structural as well as petrophysical, PVT, SCAL, etc. data may be highly uncertain and the history matching process rarely tackles all these parameters in a single step. Classically, some of these parameters are considered as known while others are updated. This constitutes the ‘by default' approach as all these parameters are interdependent and it may lead to sub-optimal history matched models.
This manuscript presents an original history matching workflow that picks uncertain structural and petrophysical parameters anywhere in the "geomodeling to simulation?? workflow, using a popular geomodeling software. Efficient parameterization technique of the geological model allows both geological and simulation models to be updated at the same time, preserving the consistency between each other. Using a versatile assisted history matching software, any external software such as a geomodeling software, may be automatically launched in batch mode from the constructed workflow. Background scripts then control each building step of the geological and simulation models, possibly capitalizing on an existing geomodel. This joint structural and petrophysical history matching leads to a more robust integrated geological stochastic reservoir model, as all uncertainties are simultaneously tackled and reduced.
The results obtained on a 3D faulted synthetic waterflooding scenario demonstrate that this history matching approach is efficient since horizon depths, throw and transmissivity of faults as well as facies distribution, petrophysical and SCAL properties are simultaneously updated to explain the production history.
One of the main outputs of reservoir engineering technical studies is to get reliable production forecasts. Within that framework, history matching of reservoir model(s) is pivotal but eventually not sufficient (Carter et al. 2006): key reservoir model inputs are updated until a satisfactory match is obtained between simulated and observed data.
But the history matching process is an under-determined inverse problem. One will never gather enough data to constrain a unique reservoir model and potentially many models explain the data equally well. All of them should be considered for the production forecasts process.
Moreover, the history matching criteria investigated has a non-smooth shape with many minima. This is a consequence of geological modeling and multiphase fluid flow simulations, based on non-linear coupled equations. It makes more complex the optimization process associated to the history matching loop. This problem is exacerbated when dealing with facies modeling as well as structural inputs, which is the case in the proposed application.
History matching of structurally complex reservoirs may appear challenging because the uncertainty in reservoir geometry may impact production forecasts order(s) of magnitude bigger than the petrophysical related one. Structural uncertainty may reside in the poor quality of seismic data. Seismic data processing, migration, interpretation results as well as time depth conversion are themselves not unique, relying on subjective choices. In such case, traditional history matching approach considers reservoir geometry as fixed during the optimization process, updating the sole petrophysical and fluids related ones. Considering some parameters as constant (and thus artificially no more uncertain) while updating remaining ones may lead to sub-optimal history matched models.
One of the main concern in the O&G business is generating reliable production profile forecasts. Such profiles are the cornerstones of optimal technico-economical management decisions. A workflow combining different methodologies to integrate and reduce most of the subsurface uncertainties using multiple history matched models (explaining the past) to infer reasonably reliable production forecasts is proposed.
Using experimental design theory, a sensitivity study is first performed to scan the whole range of static and dynamic uncertain parameters using a proxy-model of the fluid flow simulator. Only the most sensitive ones with respect to an objective function (quantifying the mismatch between the simulation results and the observations) are retained for subsequent steps.
Assisted History Matching tools are then used to get multiple History matched models, an order of magnitude faster than traditional History Matching processes. Updated uncertain parameters (selected from the sensitivity studies) may be picked anywhere in the direct problem building workflow.
Using the Bayesian framework, a posterior distribution of the most sensitive parameters are derived from the a priori distributions and a non-linear proxy model of the likelihood function. The later is computed using experimental design, kriging and dynamic training techniques.
Multiple History Matched models together with a posteriori parameter distributions are finally used in a joint modeling approach to capture the main uncertainties and to obtain typical (P10-P90) probabilistic production profiles.
This workflow has been applied to a gas storage real case submitted to significant seasonal pressure variations. Probabilistic operational pressure profiles for a given period can then be compared to the actual gas storage dynamic behaviour to assess the added value of the proposed workflow.
Copyright 2006, Society of Petroleum Engineers This paper was prepared for presentation at the SPE Europec/EAGE Annual Conference and Exhibition held in Vienna, Austria, 12-15 June 2006. This paper was selected for presentation by an SPE Program Committee following review of information contained in an abstract submitted by the author(s). Contents of the paper, as presented, have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). Papers presented at SPE meetings are subject to publication review by Editorial Committees of the Society of Petroleum Engineers. Electronic reproduction, distribution, or storage of any part of this paper for commercial purposes without the written consent of the Society of Petroleum Engineers is prohibited.