Using production data and time domain seismic attributes for history matching

2007 ◽  
Vol 284 (1) ◽  
pp. 147-159 ◽  
Author(s):  
A. Fornel ◽  
M. Mezghani ◽  
V. Langlais
Keyword(s):  
Time Domain ◽  
History Matching ◽  
Seismic Attributes ◽  
Production Data

Integrated Rock and Fluid Workflow to Optimize Geomodeling and History Matching

2021 ◽  
Author(s):  
Elizabeth Ruiz ◽  
Brandon Thibodeaux ◽  
Christopher Dorion ◽  
Herman Mukisa ◽  
Majid Faskhoodi ◽  
...  
Keyword(s):  
History Matching ◽  
Field Extension ◽  
Production Data ◽  
Geologic Time ◽  
Property Estimation ◽  
Reservoir Fluid ◽  
Fault Block ◽  
Geological Information ◽  
Geodynamic Processes ◽  
Middle Pliocene

Abstract Optimized geomodeling and history matching of production data is presented by utilizing an integrated rock and fluid workflow. Facies identification is performed by use of image logs and other geological information. In addition, image logs are used to help define structural geodynamic processes that occurred in the reservoir. Methods of reservoir fluid geodynamics are used to assess the extent of fluid compositional equilibrium, especially the asphaltenes, and thereby the extent of connectivity in these facies. Geochemical determinations are shown to be consistent with measurements of compositional thermodynamic equilibrium. The ability to develop the geo-scenario of the reservoir, the coherent evolution of rock and contained fluids in the reservoir over geologic time, improves the robustness of the geomodel. In particular, the sequence of oil charge, compositional equilibrium, fault block throw, and primary biogenic gas charge are established in this middle Pliocene reservoir with implications for production, field extension,and local basin exploration. History matching of production data prove the accuracy of the geomodel; nevertheless, refinements to the geomodel and improved history matching were obtained by expanded deterministic property estimation from wireline log and other data. Theearly connection of fluid data, both thermodynamic and geochemical, with relevant facies andtheir properties determination enables a more facile method to incorporate this data into the geomodel. Logging data from future wells in the field can be imported into the geomodel allowingdeterministic optimization of this model long after production has commenced. While each reservoir is unique with its own idiosyncrasies, the workflow presented here is generally applicable to all reservoirs and always improves reservoir understanding.

Global-Search Distributed-Gauss-Newton Optimization Method and Its Integration With the Randomized-Maximum-Likelihood Method for Uncertainty Quantification of Reservoir Performance

SPE Journal ◽  
2018 ◽  
Vol 23 (05) ◽  
pp. 1496-1517 ◽  
Author(s):  
Chaohui Chen ◽  
Guohua Gao ◽  
Ruijian Li ◽  
Richard Cao ◽  
Tianhong Chen ◽  
...  
Keyword(s):  
History Matching ◽  
Optimization Method ◽  
Global Search ◽  
Real Field ◽  
Production Data ◽  
Finite Difference Approximations ◽  
Production Forecasting ◽  
Novel Method ◽  
Randomized Maximum Likelihood ◽  
Search Capability

Summary Although it is possible to apply traditional optimization algorithms together with the randomized-maximum-likelihood (RML) method to generate multiple conditional realizations, the computation cost is high. This paper presents a novel method to enhance the global-search capability of the distributed-Gauss-Newton (DGN) optimization method and integrates it with the RML method to generate multiple realizations conditioned to production data synchronously. RML generates samples from an approximate posterior by minimizing a large ensemble of perturbed objective functions in which the observed data and prior mean values of uncertain model parameters have been perturbed with Gaussian noise. Rather than performing these minimizations in isolation using large sets of simulations to evaluate the finite-difference approximations of the gradients used to optimize each perturbed realization, we use a concurrent implementation in which simulation results are shared among different minimization tasks whenever these results are helping to converge to the global minimum of a specific minimization task. To improve sharing of results, we relax the accuracy of the finite-difference approximations for the gradients with more widely spaced simulation results. To avoid trapping in local optima, a novel method to enhance the global-search capability of the DGN algorithm is developed and integrated seamlessly with the RML formulation. In this way, we can improve the quality of RML conditional realizations that sample the approximate posterior. The proposed work flow is first validated with a toy problem and then applied to a real-field unconventional asset. Numerical results indicate that the new method is very efficient compared with traditional methods. Hundreds of data-conditioned realizations can be generated in parallel within 20 to 40 iterations. The computational cost (central-processing-unit usage) is reduced significantly compared with the traditional RML approach. The real-field case studies involve a history-matching study to generate history-matched realizations with the proposed method and an uncertainty quantification of production forecasting using those conditioned models. All conditioned models generate production forecasts that are consistent with real-production data in both the history-matching period and the blind-test period. Therefore, the new approach can enhance the confidence level of the estimated-ultimate-recovery (EUR) assessment using production-forecasting results generated from all conditional realizations, resulting in significant business impact.

Using time domain seismic data for history matching processes

First Break ◽  
2011 ◽  
Vol 29 (1770) ◽  
Author(s):  
E. Tillier ◽  
M. Le Ravalec ◽  
F. Roggero
Keyword(s):  
Time Domain ◽  
Seismic Data ◽  
History Matching

History Matching and Rate Forecasting in Unconventional Oil Reservoirs With an Approximate Analytical Solution to the Double-Porosity Model

2015 ◽  
Vol 19 (01) ◽  
pp. 070-082 ◽  
Author(s):  
B. A. Ogunyomi ◽  
T. W. Patzek ◽  
L. W. Lake ◽  
C. S. Kabir
Keyword(s):  
Analytical Solution ◽  
History Matching ◽  
Horizontal Wells ◽  
Double Porosity ◽  
Unconventional Reservoirs ◽  
Production Data ◽  
Time Space ◽  
Double Porosity Model ◽  
Fractured Horizontal Wells ◽  
Porosity Model

Summary Production data from most fractured horizontal wells in gas and liquid-rich unconventional reservoirs plot as straight lines with a one-half slope on a log-log plot of rate vs. time. This production signature (half-slope) is identical to that expected from a 1D linear flow from reservoir matrix to the fracture face, when production occurs at constant bottomhole pressure. In addition, microseismic data obtained around these fractured wells suggest that an area of enhanced permeability is developed around the horizontal well, and outside this region is an undisturbed part of the reservoir with low permeability. On the basis of these observations, geoscientists have, in general, adopted the conceptual double-porosity model in modeling production from fractured horizontal wells in unconventional reservoirs. The analytical solution to this mathematical model exists in Laplace space, but it cannot be inverted back to real-time space without use of a numerical inversion algorithm. We present a new approximate analytical solution to the double-porosity model in real-time space and its use in modeling and forecasting production from unconventional oil reservoirs. The first step in developing the approximate solution was to convert the systems of partial-differential equations (PDEs) for the double-porosity model into a system of ordinary-differential equations (ODEs). After which, we developed a function that gives the relationship between the average pressures in the high- and the low-permeability regions. With this relationship, the system of ODEs was solved and used to obtain a rate/time function that one can use to predict oil production from unconventional reservoirs. The approximate solution was validated with numerical reservoir simulation. We then performed a sensitivity analysis on the model parameters to understand how the model behaves. After the model was validated and tested, we applied it to field-production data by partially history matching and forecasting the expected ultimate recovery (EUR). The rate/time function fits production data and also yields realistic estimates of ultimate oil recovery. We also investigated the existence of any correlation between the model-derived parameters and available reservoir and well-completion parameters.

scholarly journals An alternative approach to match field production data from unconventional gas-bearing systems

2020 ◽  
Vol 17 (5) ◽  
pp. 1370-1388
Author(s):  
Zhi-Gang Zhang ◽  
Yan-Bao Liu ◽  
Hai-Tao Sun ◽  
Wei Xiong ◽  
Kai Shen ◽  
...  
Keyword(s):  
History Matching ◽  
Field Data ◽  
Ordos Basin ◽  
Production Data ◽  
Unconventional Gas ◽  
Alternative Approach ◽  
Matching Techniques ◽  
Gas Bearing ◽  
Bearing System ◽  
Automatic Computing

Abstract Nowadays, the unconventional gas-bearing system plays an increasingly important role in energy market. The performances of the current history-matching techniques are not satisfied when applied to such systems. To overcome this shortfall, an alternative approach was developed and applied to investigate production data from an unconventional gas-bearing system. In this approach, the fluid flow curve obtained from the field is the superposition of a series of Gaussian functions. An automatic computing program was developed in the MATLAB, and both gas and water field data collected from a vertical well in the Linxing Block, Ordos Basin, were used to present the data processing technique. In the reservoir study, the automatic computing program was applied to match the production data from a single coal seam, multiple coal seams and multiple vertically stacked reservoirs with favourable fitting results. Compared with previous approaches, the proposed approach yields better results for both gas and water production data and can calculate the contributions from different reservoirs. The start time of the extraction for each gas-containing unit can also be determined. The new approach can be applied to the field data prediction and designation for the well locations and patterns at the reservoir scale.

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