Joint history matching of seismic and production data

History matching is the process of building one or more sets of numerical models (representing a reservoir) which account for observed, measured data. The reason why we want to calibrate the subsurface model is because we want to use it to reliably predict the future reservoir behavior. 

Challenges

Reservoir properties are only truely known at well locations. In the geomodeling workflow, we use different geostatistic algorithms to populate models in-between and beyond wells. Standard history matching methods mainly match the models to production data or other data measured at wells, assuming that the matched models shall produce robust prediction. On production profile forecasting, this may be true because matched models may produce equivalent flow behavior at well locations to that of the real reservoir.  However, if the objectives of is to identify by-passed oil pockets in order to deterministically pin down infill drilling targets, or to decisively determine which wells to shut in to slow down water encroachment, or to re-perforate to access remaining reserves, having only flow-equivalent geostatistical models will often not be sufficient. Instead, the focus of the reservoir updating effort should be switched to increase the geological “trueness” of the models by jointly matching the models with both production and seismic data. 

Benefits

  • Unrivaled accuracy in locating the remaining oil enabling optimal selection of infill drilling targets, cost-effective production optimization and EOR activities
  • Reduce the cost and turnaround time of standard history matching projects by reducing the number of variables, and by allowing reservoir engineers to focus on the most important variables
  • Better decision-making through improved “Trueness” of the reservoir model and reduced uncertainties  (smaller ensemble) by seismic history matching

Promoters of sophisticated history matching packages often assert that a particular method honors all reservoir data and accounting for all uncertainties. However, the truth is that most of history matching packages in the market today focus on history matching of well data, resulting in biased results. More importantly, even the latest sophisticated methods (including those based on big-loop and ensemble-based method) do not completely honor seismic data. This is understandable because to calibrate the model with seismic data in a closed-loop fashion is a very challenging task in history matching. As the result, the synthetic seismic cubes generated based on the ensemble of matched models often exhibit large discrepancies in comparison with observed seismic. The discrepancies suggest low “trueness” of the geostatistical models even if they are matched to production data. This explains why many asset management teams still lack confidence in reservoir models after expensive and lengthy history matching projects are implemented. 

 

Approach

It is the common objective of all reservoir model updating projects to integrate all reservoir data (both static and dynamic). However, there is no single approach that is approximate for all circumstances. Depending on the quality of different types of reservoir data available, and the existing reservoir modelling strategy, there are many ways to condition reservoir models to seismic data while maintaining the matching with production data. 

Solution 1 – Manual updating

Despite an increasing number of sophisticated history matching tools emerging in recent years allowing users to generate a suite of realization rather than a single reservoir model in history-matching research. For many asset teams, updating the single reservoir model manually would still seem to be a sensible choice under practical conditions when taking into account the following factors:

  • Cost 
  • Time and effort taken to set up the sophisticated tool, train up the users, and examine the multiple scenarios and realizations
  • Lack of “Trueness” of the geostatistical reservoir models only matched to historic production data
  • Uncertainties are almost certainly under-estimated regardless of history matching methods
Joint product and seis

iRes-Geo leads the way in the development of software for joint production and seismic history matching. Closed-loop™ offers a suite of useful functionalities, such as co-visualization, data management and model updating functionalities, enabling asset teams to easily detect the problems with the reservoir model and make the necessary changes.

Approach

It is the common objective of all reservoir model updating projects to integrate all reservoir data (both static and dynamic). However, there is no single approach that is approximate for all circumstances. Depending on the quality of different types of reservoir data available, and the existing reservoir modelling strategy, there are many ways to condition reservoir models to seismic data while maintaining the matching with production data. 

Solution 1 – Manual updating

Despite an increasing number of sophisticated history matching tools emerging in recent years allowing users to generate a suite of realization rather than a single reservoir model in history-matching research. For many asset teams, updating the single reservoir model manually would still seem to be a sensible choice under practical conditions when taking into account the following factors:

  • Cost 
  • Time and effort taken to set up the sophisticated tool, train up the users, and examine the multiple scenarios and realizations
  • Lack of “Trueness” of the geostatistical reservoir models only matched to historic production data
  • Uncertainties are almost certainly under-estimated regardless of history matching methods
Joint product and seis

iRes-Geo leads the way in the development of software for joint production and seismic history matching. Closed-loop™ offers a suite of useful functionalities, such as co-visualization, data management and model updating functionalities, enabling asset teams to easily detect the problems with the reservoir model and make the necessary changes.

Solution 2 – Small-Loop approach

It is the common objective of all reservoir model updating projects to integrate all reservoir data (both static and dynamic). However, there is no single approach that is approximate for all circumstances. Depending on the quality of different types of reservoir data available, and the existing reservoir modelling strategy, there are many ways to utilize Closed-loop™ to condition reservoir models to seismic data. 

For all sorts of reasons – logistical, technical, management, contractual, it is not always easy for reservoir engineers to go back and adjust the geological model in a close cooperative environment. This approach is often referred to as small loop history matching. Under this circumstances, it is quite common for reservoir engineers to adjust the geological modelling without recoursing to the geologists by multiplying the porosity, the permeability, the anisotropy (kv/kh), the relative permeabilities, the well factors and many other parameters within their numerical world. Sometimes these factors can be large and global and probably outside the limits of the geological reality. When tied in with small-loop workflow, seismic Closed-loop™ is a good solution to reduce parameterization and ensure geological consistency of matched simulation models. 

 

Option 1Seismic Closed-loop™ + Small-loop history matching

Solution 2 – Small-Loop approach

For all sorts of reasons – logistical, technical, management, contractual, it is not always easy for reservoir engineers to go back and adjust the geological model in a close cooperative environment. This approach is often referred to as small loop history matching. Under this circumstances, it is quite common for reservoir engineers to adjust the geological modelling without recoursing to the geologists by multiplying the porosity, the permeability, the anisotropy (kv/kh), the relative permeabilities, the well factors and many other parameters within their numerical world. Sometimes these factors can be large and global and probably outside the limits of the geological reality. When tied in with small-loop workflow, seismic Closed-loop™ is a good solution to reduce parameterization and ensure geological consistency of matched simulation models. 

 

Option 1Seismic Closed-loop™ + Small-loop history matching

Solution 3 – Big loop approach

iRes-Geo’s Closed-loop™ delivers the highest level of accuracy in simulator-to-seismic modelling with proprietary SmartGrid technology. Different simulation models can be hooked to Closed-loop™ platform. The user can import simulation models/results ( Eclipse, VIP, CMG or Str3D models) to Closed-loop ™. Also, from Closed-loop™, the user can export updated simulation models and launch new simulation runs. iRes-Geo leads the way in its unique combined approach that allows for quick generation of synthetic seismic data for a large ensemble of reservoir models, and efficient model refinement if necessary. Also, Closed-loop™ offers a variety of methods to analyze the difference between observed and modelled seismic in both the seismic and the reservoir model domain. The seismic screening method can be tied in big-loop and ensemble-based model optimization methods to eliminate the models with low geological “trueness”. Most importantly, our software offer a wide range of functionalities to further refine the matched models after the seismic screening.

 

Option 2Seismic screening of the ensemble of matched reservoir simulation models

Different schemes made available by Closed-loop™ for mismatch evaluation

 

Solution 3 – Big loop approach

iRes-Geo’s Closed-loop™ delivers the highest level of accuracy in simulator-to-seismic modelling with proprietary SmartGrid technology. Different simulation models can be hooked to Closed-loop™ platform. The user can import simulation models/results ( Eclipse, VIP, CMG or Str3D models) to Closed-loop ™. Also, from Closed-loop™, the user can export updated simulation models and launch new simulation runs. iRes-Geo leads the way in its unique combined approach that allows for quick generation of synthetic seismic data for a large ensemble of reservoir models, and efficient model refinement if necessary. Also, Closed-loop™ offers a variety of methods to analyze the difference between observed and modelled seismic in both the seismic and the reservoir model domain. The seismic screening method can be tied in big-loop and ensemble-based model optimization methods to eliminate the models with low geological “trueness”. Most importantly, our software offer a wide range of functionalities to further refine the matched models after the seismic screening.

 

Option 2 – Seismic screening of the ensemble of matched reservoir simulation models