Application of Saturation-height Functions in Integrated Reservoir Description

2002 ◽  
Author(s):  
Paul F. Worthington
Keyword(s):  
Height Functions ◽  
Reservoir Description

Saturation-Height Functions

2014 ◽  
Author(s):  
John H. Doveton
Keyword(s):  
Prediction Models ◽  
Stratigraphic Correlation ◽  
Reservoir Model ◽  
Sweep Efficiency ◽  
Height Functions ◽  
Reservoir Architecture ◽  
Flow Characterization ◽  
Reservoir Conditions ◽  
Reservoir Description ◽  
Engineering Practices

As observed by Worthington (2002), “The application of saturation-height functions forms part of the intersection of geologic, petrophysical, and reservoir engineering practices within integrated reservoir description.” It is also a critical reference point for mathematical petrophysics; the consequences of deterministic and statistical prediction models are finally evaluated in terms of how closely the estimates conform to physical laws. Saturations within a reservoir are controlled by buoyancy pressure applied to pore-throat size distributions and pore-body storage capacities within a rock unit that varies both laterally and vertically and may be subdivided into compartments that are not in pressure communication. Traditional lithostratigraphic methods describe reservoir architecture as correlative rock units, but the degree to which this partitioning matches flow units must be carefully evaluated to reconcile petrofacies with lithofacies. Stratigraphic correlation provides the fundamental reference framework for surfaces that define structure and isopach maps and usually represent principal reflection events in the seismic record. In some instances, there is a strong conformance between lithofacies and petrofacies, but all too commonly, this is not the case, and petrofacies must be partitioned and evaluated separately. Failure to do this may result in invalid volumetrics and reservoir models that are inadequate for fluid-flow characterization. A dynamic reservoir model must be history matched to the actual performance of the reservoir; this process often requires adjustments of petrophysical parameters to improve the reconciliation between the model’s performance and the history of production. Once established, the reservoir model provides many beneficial outcomes. At the largest scale, the model assesses the volumetrics of hydrocarbons in place. Within the reservoir, the model establishes any partitioning that may exist between compartments on the basis of pressure differences and, therefore, lack of communication. Lateral trends within the model trace changes in rock reservoir quality that control anticipated rates and types of fluids produced in development wells. Because the modeled fluids represent initial reservoir conditions, comparisons can be made between water saturations of the models and those calculated from logs in later wells, helping to ascertain sweep efficiency during production.

An Integrated Model for Computer Aided Reservoir Description : from Outcrop Study to Fluid Flow Simulations

1990 ◽  
Vol 45 (1) ◽  
pp. 71-77 ◽  
Author(s):  
D. Guerillot ◽  
J. L. Rudkiewicz ◽  
C. Ravenne ◽  
G. Renard ◽  
A. Galli
Keyword(s):  
Fluid Flow ◽  
Integrated Model ◽  
Flow Simulations ◽  
Computer Aided ◽  
Reservoir Description

Expanding the Envelope of Fiber-Optic Sensing for Reservoir Description and Dynamics

2021 ◽  
Author(s):  
Abdulaziz Al-Qasim ◽  
Sharidah Alabduh ◽  
Muhannad Alabdullateef ◽  
Mutaz Alsubhi
Keyword(s):  
Performance Optimization ◽  
Fiber Optic ◽  
Thermal Recovery ◽  
Production Performance ◽  
Flow Profile ◽  
Distributed Temperature Sensing ◽  
Integrity Monitoring ◽  
Well Integrity ◽  
Fiber Optic Sensing ◽  
Reservoir Description

Abstract Fiber-optic sensing (FOS) technology is gradually becoming a pervasive tool in the monitoring and surveillance toolkit for reservoir engineers. Traditionally, sensing with fiber optic technology in the form of distributed temperature sensing (DTS) or distributed acoustic sensing (DAS), and most recently distributed strain sensing (DSS), distributed flow sensing (DFS) and distributed pressure sensing (DPS) were done with the fiber being permanently clamped either behind the casing or production tubing. Distributed chemical sensing (DCS) is still in the development phase. The emergence of the composite carbon-rod (CCR) system that can be easily deployed in and out of a well, similar to wireline logging, has opened up a vista of possibilities to obtain many FOS measurements in any well without prior fiber-optic installation. Currently, combinations of distributed FOS data are being used for injection management, well integrity monitoring, well stimulation and production performance optimization, thermal recovery management, etc. Is it possible to integrate many of the distributed FOS measurements in the CCR or a hybrid combination with wireline to obtain multiple measurements with one FOS cable? Each one of FOS has its own use to get certain data, or combination of FOS can be used to make a further interpretation. This paper reviews the state of the art of the FOS technology and the gamut of current different applications of FOS data in the oil and gas (upstream) industry. We present some results of traditional FOS measurements for well integrity monitoring, assessing production and injection flow profile, cross flow behind casing, etc. We propose some nontraditional applications of the technology and suggest a few ways through. Which the technology can be deployed for obtaining some key reservoir description and dynamics data for reservoir performance optimization.

Study on 3D Fine Structural Modeling of Typical Block in Sanan Oilfield, Daqing

2014 ◽  
Vol 628 ◽  
pp. 348-353
Author(s):  
Tao Li ◽  
Zian Li ◽  
Jiang Wang
Keyword(s):  
Structural Model ◽  
3D Structure ◽  
High Water ◽  
Oil Distribution ◽  
Remaining Oil ◽  
Modeling Software ◽  
Remaining Oil Distribution ◽  
High Water Cut ◽  
Reservoir Description ◽  
Water Cut

Sanan oilfield has entered late stage of high water cut development. It urgently needs accurate prediction of remaining oil distribution. But previous studies on 3D structure were far could not meet the requirements of fine reservoir description. This paper applied RMS, a piece of excellent geological modeling software establishing the 3D fine structural model of typical block in Sanan oilfield on the bases of 3D fine seismic structural interpretation data. It included the 28 faults’ model, 11 horizons’ model and the structural model. And then measured and analyzed the faults elements data. Based on abundant geologic data, well data and seismic data of the block, this structural model reproduced the fine seismic interpretation results accurately. It was really fine enough to meet the requirements of the fine reservoir description. This research solved the problem that traditional modeling techniques could not handle complex cutting relationship of faults’ model. It laid a solid foundation for reservoir numerical simulation and remaining oil distribution prediction.

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