Joint Structural and Petrophysical History Matching Leads to Global Geological Stochastic Reservoir Models

Abstract

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. Introduction 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.

New hybrid Cartesian cut cell/enriched multipoint flux approximation approach for modelling and quantification of structural uncertainties in petroleum reservoirs

“…Works on quantification of structural uncertainties were initiated with more conveniently operational structural parameters like the reservoir depth, layer thickness and aquifer depth. Schaaf et al [24] have investigated the influence of uncertainty in reservoir and aquifer depth and thickness on gas storage forecasts. Rivenaes et al [17] have considered the fault structural uncertainty by creating stochastic alternative realisations of fault pattern.…”

“…In Palatnik et al [22], such structural parameters have been parameterised as region multipliers, and a conjugate gradient method has been applied to minimise the objective function in history matching process. In another work, Schaaf et al [24] presented an automatic workflow for simultaneous updating of structural and petrophysical reservoir properties. Structural parameters of each fault pattern realisation then were encapsulated and reduced to transmissibility multipliers.…”

“…Indeed, this approach is very likely to fail in case of complex reservoir geometry encompassing several uncertain geological structures, as creating a representative ensemble that spans high-dimensional uncertain parameter space is very time-consuming and consequently tends to be impractical. Schaaf et al [24] have investigated the influence of uncertainty in reservoir and aquifer depth and thickness on gas storage forecasts. In this work, the variation of reservoir and aquifer thickness in history matching workflow has been incorporated with varying volume cells in a 1-D reservoir model.…”

Adjoint-Based History Matching of Structural Models Using Production and Time-Lapse Seismic Data

“…In this paper we do not employ a full Big Loop workflow, but, instead, we focus on updating some of the structural aspects of the static model. Such an approach to update structural parameters has been addressed in several studies before (Rivenaes et al, 2005;Suzuki et al, 2008;Schaaf et al, 2009;Seiler et al, 2010;Skjervheim et al, 2012). Rivenaes et al (2005) generated various fault patterns and ran streamline simulations for the entire set of realizations.…”

“…Suzuki et al (2008) built a large set of models that covered a wide range of possible structural interpretations, and subsequently used stochastic search methods to find those realizations that matched the historical production data. Schaaf et al (2009) presented a workflow that updates both geological and simulation models at the same time using two different optimization methods. Synthetic historical production data were assimilated in their workflow.…”