Conditioning Integrated Geological Models to Dynamic Flow Data of Giant Saudi Arabian Reservoir

Qassab, Hisham M. Al; Rahmeh, Basem A.; Khalifa, Mohammed; Awami, Bassam A. Al; Sarkar, Amit Kumar

doi:10.2118/71319-ms

“…[1][2][3][4][5][6][7] In spite of these developments, large-scale field application of these methods accounting for realistic field conditions has been very limited to date. [8][9] The primary difficulty here is the non-linear relationship between the production data and the reservoir model parameters such as permeability. This makes the inverse problem related to history matching an iterative procedure, requiring at least an order of magnitude more computation time than the flow simulation by itself.…”

Section: Introductionmentioning

confidence: 99%

Streamline-based Production Data Integration Under Realistic Field Conditions: Experience in a Giant Middle-Eastern Reservoir

Qassab

¹

,

Pavlas

²

,

Afaleg

³

et al. 2003

SPE Annual Technical Conference and Exhibition

View full text Add to dashboard Cite

In this paper we apply the streamline-based production data integration method to condition a multimillion cell geologic model to historical production response for a giant Saudi Arabian reservoir. The field has been under peripheral water injection with 16 injectors and 70 producers. There is also a strong aquifer influx into the field. A total of 30 years of production history with detailed rate, infill well and reperforation schedule were incorporated via multiple pressure updates during streamline simulation. Also, gravity and compressibility effects were included to account for water slumping and aquifer support. To our knowledge, this is the first and the largest such application of production data integration to geologic models accounting for realistic field conditions. We have developed novel techniques to analytically compute the sensitivities of the production response in the presence of gravity and changing field conditions. This makes our method extremely computationally efficient. For the field application, the production data integration is carried out in less than 6 hours in a PC.The geologic model derived after conditioning to production response was validated using field surveillance data. In particular, the flood front movement, the aquifer encroachment and bypassed oil locations obtained from the geologic model was found to be consistent with field observations. Finally, an examination of the permeability changes during production data integration revealed that most of these changes were aligned along the facies distribution, particularly the 'good' facies distribution with no resulting loss in geologic realism. TX 75083-3836, U.S.A., fax 01-972-952-9435.

show abstract

“…The field ranks 22 nd largest in the world and is in a mature state of development with more than 30 years of production history. Geologic (facies), porosity log and acoustic impedance data from a 3-D stochastic seismic inversion model were integrated to generate the initial fine-scale (128 layer, 2.6 million cells) reservoir model 9 . The streamline-based travel time inversion was used to condition the reservoir model to the water-cut history for 30 years.…”

Section: Spe 84079mentioning

confidence: 99%

“…The initial geologic model was created based on well log derived porosity, facies information and 3-D seismic data. 9 The 11 different facies types were combined into 6 facies groups in the order of decreasing quality and having distinct porosity-permeability relationship. Then a 3-D facies model was generated using indicator kriging (Fig.…”

Section: Field Application: Geologic and Simulation Modelmentioning

confidence: 99%

“…Multiple realizations of porosity distribution in the reservoir were generated using facies-based sequential Gaussian cosimulation with well data guided by the seismic acoustic impedance as a secondary variable. 9 The particular porosity distribution used in this study is shown in Fig. 2.…”

Section: Field Application: Geologic and Simulation Modelmentioning

confidence: 99%

“…[1][2][3][4][5][6][7] In spite of these developments, large-scale field application of these methods accounting for realistic field conditions has been very limited to date. [8][9] The primary difficulty here is the non-linear relationship between the production data and the reservoir model parameters such as permeability. This makes the inverse problem related to history matching an iterative procedure, requiring at least an order of magnitude more computation time than the flow simulation by itself.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Streamline-based Production Data Integration Under Realistic Field Conditions: Experience in a Giant Middle-Eastern Reservoir

Qassab¹,

Khalifa²,

Afaleg³

et al. 2003

Proceedings of SPE Annual Technical Conference and Exhibition

View full text Add to dashboard Cite

In this paper we apply the streamline-based production data integration method to condition a multimillion cell geologic model to historical production response for a giant Saudi Arabian reservoir. The field has been under peripheral water injection with 16 injectors and 70 producers. There is also a strong aquifer influx into the field. A total of 30 years of production history with detailed rate, infill well and reperforation schedule were incorporated via multiple pressure updates during streamline simulation. Also, gravity and compressibility effects were included to account for water slumping and aquifer support. To our knowledge, this is the first and the largest such application of production data integration to geologic models accounting for realistic field conditions. We have developed novel techniques to analytically compute the sensitivities of the production response in the presence of gravity and changing field conditions. This makes our method extremely computationally efficient. For the field application, the production data integration is carried out in less than 6 hours in a PC.The geologic model derived after conditioning to production response was validated using field surveillance data. In particular, the flood front movement, the aquifer encroachment and bypassed oil locations obtained from the geologic model was found to be consistent with field observations. Finally, an examination of the permeability changes during production data integration revealed that most of these changes were aligned along the facies distribution, particularly the 'good' facies distribution with no resulting loss in geologic realism. TX 75083-3836, U.S.A., fax 01-972-952-9435.

show abstract

Impact of Facies Modeling on Reservoir Performance Forecasting: a Comparison between Discrete Modeling and Mixed Lithology Approaches

Al-Qassab

¹

,

Vaughan

²

2012

All Days

2

0

View full text Add to dashboard Cite

Facies modeling forms an integral part of geological numerical modeling. Over the last two decades, different facies modeling methods have been developed using geostatistical algorithms. Most of these methods rely on the assumption of discrete or binary modeling during which each model cell is assigned a single facies. In this study, the size of the cells is on average 100 meters by 100 meters laterally by one meter thick. Based on comparisons to outcrops and subsurface data, such cells should, in fact, include a mixture of facies. The discrete-facies approach assumes a single facies per cell. The distribution of the facies between wells is described using classical categorical geostatistical algorithms. Reservoir properties are then populated by facies within mapped environments of deposition. This process is well-established and straightforward, especially with regard to tying well data, handling property trends, and applying net rock cut-offs. A mixed-facies approach can be performed using effective property modeling in which multiple small, fine-scale models are built for each environment of deposition. These models are re-sampled to the full-field cell volume using static and flow-based upscaling methods. The resulting statistics are then used with geostatistics, conditioned to the proportion of each facies present, to populate the full-field model. Such models allow the incorporation of core-scale heterogeneity potentially important in improved oil recovery projects, and may reduce modeling cycle times, especially when multiple iterations are required, such as during history-matching or uncertainty analysis. This paper compares the impact on simulated fluid flow of modeling facies using discrete modeling versus a mix of facies per cell. Shoreface and subordinate fluvial environments of deposition facies, and five reservoir lithofacies, were modeled. Fluid-flow simulation of the mixed-facies model, under both primary depletion and pressure maintenance conditions, was smooth and uniform, with a highly conformable flood front. The discrete model was more stratified, with faster and less conformable water movement. The assignment of discrete facies to large model cells (few hundred meters laterally & few meters vertically) takes less time than a mixed-facies approach and does a better job of preserving organized extremes of permeability important at the production timescale. In the early stages of field development, when there is much uncertainty and a rapid, scenario-based modeling approach is desirable, the discrete approach can be used to flag heterogeneity-related risks more quickly and confidently than the mixed-facies technique. Inaccuracies in performance parameters resulting from the assignment of unscaled discrete values can be corrected using fine-scale sector models tailored to the highest risk cases.

show abstract

Conditioning Integrated Geological Models to Dynamic Flow Data of Giant Saudi Arabian Reservoir

Cited by 5 publications

References 2 publications

Streamline-based Production Data Integration Under Realistic Field Conditions: Experience in a Giant Middle-Eastern Reservoir

Streamline-based Production Data Integration Under Realistic Field Conditions: Experience in a Giant Middle-Eastern Reservoir

Streamline-based Production Data Integration Under Realistic Field Conditions: Experience in a Giant Middle-Eastern Reservoir

Impact of Facies Modeling on Reservoir Performance Forecasting: a Comparison between Discrete Modeling and Mixed Lithology Approaches

Contact Info

Product

Resources

About