History matching

Once production commences, data such as reservoir pressure, cumulative production, GOR, water cut and fluid contact movement are collected, and may be used to history match the simulation model. This entails adjusting the reservoir model to fit the observed data. The updated model may then be used for a more accurate prediction of future performance. This procedure is cyclic, and a full field reservoir simulation model will be updated whenever a significant amount of new data becomes available (say, every two to five years). 

The reservoir model will usually be a computer based simulation model, such as the 3D model described in Section 8. As production continues, the monitoring programme generates a data base containing information on the performance of the field. The reservoir model is used to check whether the initial assumptions and description of the reservoir were correct. Where inconsistencies between the predicted and observed behaviour occur, the model is reviewed and adjusted until a new match (a so-called history match ) is achieved. The updated model is then used to predict future performance of the field, and as such is a very useful tool for generating production forecasts. In addition, the model is used to predict the outcome of alternative future development plans. The criterion used for selection is typically profitability (or any other stated objective of the operating company). 

Much of the variation in these time series for the past 700 kyr can be described by a combination of a 100 kyr cycle plus additional cycles with periods of 20 and 40 kyr. This result immediately suggests that the ice-age cycles are caused by variations in the amount and seasonality of solar radiation reaching the Earth (insolation), because the 20, 40, and 100 kyr periods of climate history match the periods of cyclic variations in Earth s orbit and axial tilt, line hypothesis that these factors control climate was proposed by Milutin Milankovitch in the early part of the 20th century and is widely known as "Milankovitch Theory." It is now generally accepted that the Milankovitch variations are the root cause of the important 20 and 40 kyr climate cycles. The 100 kyr cycle, however, proves to be a puzzle. The magnitude of the insolation variation at this periodicity is relatively trivial, but the 100 kyr cycle dominates the climate history of the last 700 kyr. Further,... 

Petroleum and chemical engineers perform oil reservoir simulation to optimize the production of oil and gas. Black-oil, compositional or thermal oil reservoir models are described by sets of differential equations. The measurements consist of the pressure at the wells, water-oil ratios, gas-oil ratios etc. The objective is to estimate through history matching of the reservoir unknown reservoir properties such as porosity and permeability. 

A numerical example for the estimation of unknown parameters in PDE models is provided in Chapter 18 where we discuss automatic history matching of reservoir simulation models. 

History matching in reservoir engineering refers to the process of estimating hydrocarbon reservoir parameters (like porosity and permeability distributions) so that the reservoir simulator matches the observed field data in some optimal fashion. The intention is to use the history matched-model to forecast future behavior of the reservoir under different depletion plans and thus optimize production. 

A Fully Implicit, Three Dimensional, Three-Phase Simulator with Automatic History-Matching Capability... 

The various simulation runs revealed that the Gauss-Newton implementation by Tan and Kalogerakis (1991) was extremely efficient compared to other reservoir history matching methods reported earlier in the literature. 

Improved Reservoir Characterization Through Automatic History Matching... 

They performed an extensive case study to demonstrate the use of automatic history matching to reservoir characterization. For example, if the estimated permeability of a particular zone is unrealistically small compared to geological information, there is a good chance that an impermeable barrier is present. Similarly if the estimated porosity of a zone approaches unrealistically high values, chances are the zone of the reservoir should be expanded beyond its current boundary. 

Step 5. Run the automatic history matching model to obtain estimates of the unknown parameters. 

By using automatic history matching, the reservoir engineer is not faced with the usual dilemma whether to reject a particular grid cell model because it is not a good approximation to the reservoir or to proceed with the parameter search because the best set of parameters has not been determined yet. 

It is of interest in a reservoir simulation study to compute future production levels of the history matched reservoir under alternative depletion plans. In addition, the sensitivity of the anticipated performance to different reservoir descriptions is also evaluated. Such studies contribute towards assessing the risk associated with a particular depletion plan. 

Figure 18 28 History matched and forecasted production by Models B and C, and comparison with actual. (a) Water and Gas production, (h) Oil production [reprinted from the Journal of the Canadian Petroleum Technology with permission].

Tan, T.B. and N. Kalogerakis, "A Three-dimensional Three-phase Automatic History Matching Model Reliability of Parameter Estimates", J. Can Petr. Technology, 31(3), 34-41 (1992). 

Crockett, A.R., Willis, R.M. Jr., and Cleary, M.P. "Improvement of Hydraulic Fracture Predictions by Real-Time History Matching on Observed Pressures," SPE paper 15264, 1986 SPE Unconventional Gas Technology Symposium, Louisville, May 18-21. 

A further difficulty that faces oil field modelers is the lack of information they have about downhole conditions and reservoir characteristics. Forward predictions about production are distressingly uncertain. It is therefore common to fit observed production data retrospectively—a procedure known as history matching—and to infer reservoir parameters, particularly permeabilities and relative permeabilities. 

Design of field projects using surfactants selected in Step 7 and a combination of laboratory floods at reservoir conditions, computer simulators, and reservoir history matching. 

This equation is shown on Figure 3 as the solid lines. The coefficient of variation and the correlation length were all derived from the properties of the underlying permeability field—there was no history match. (Strictly speaking, the statistical properties should come from the velocity field, but they appear to be the same within the accuracy of determining C and X. )... 

Following this study, Wilk et al. [230] simulated the composition-time profiles for selected alkenes and oxygenated products that were formed from n-butane and i-butane combustion, and also mixtures of these fuels, in a motored engine. An engine cycle was simulated within a spatially uniform zone of varying volume. The volume history was specified in such a way that the predicted pressure history matched the measured polytropic pressure history in non-reactive conditions. Composition profiles were compared with those measured experimentally. Some of the kinetic features that distinguish the reactivities of the two fuels and their modes of reaction involving alkylperoxy and dialkylperoxy radicals were elucidated in this work. The n-butane oxidation model had also been applied to the... 

A model was built using the best estimates of the variables including volumetries, stratigraphy, leak window area, hydrocarbon and aquifer properties. By producing hydrocarbons, the pressure in one block was depleted at the measured rate while the depletion profiles of the non-producing block were monitored. The least known parameter, the transmissibility of the faults, was varied until a history match was achieved between the modelled and observed depletion profiles. Comparison for different faults showed that faults with small displacements had to be modelled with higher transmissibilities than faults with large displacements. 

Fig. 10. Flow simulation pressure history match. A simple block model was depleted by producing gas from a production well. The fault transmis-sibility varied to alter depletion rates in the non-producing block until a best-fit pressure history match was achieved. The resulting transmissibility could be related to the observed throw on the fault.

Fig. 3, History match of cases A-G and M and N over the last lOOmyr with diffusion gas balances obtained for vertical flow of methane.

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