Static Modeling at Prudhoe Bay - Integrating Reservoir Characterization and Reservoir Performance

1994 ◽  
Author(s):  
A.P. Wilson ◽  
C.D. Severson
Keyword(s):  
Reservoir Characterization ◽  
Reservoir Performance ◽  
Static Modeling ◽  
Prudhoe Bay

Dynamic to Static Modeling

Geophysics ◽  
2021 ◽  
pp. 1-43
Author(s):  
Javad Sharifi
Keyword(s):  
Reservoir Characterization ◽  
Triaxial Compression ◽  
Rock Physics ◽  
Regression Equations ◽  
Compression Tests ◽  
In Situ Tests ◽  
Factors Affecting ◽  
Static Modulus ◽  
Wide Range ◽  
Static Modeling

Dynamic-to-static modulus conversion has long been recognized as a complicated and challenging task in reservoir characterization and seismic geomechanics, and many single- and two-variable regression equations have been proposed. In practice however, the form and constants of the regression equation are variable from case to case. I introduce a methodology for estimating the static moduli called dynamic-to-static modeling (DTS). The methodology was validated by laboratory tests (ultrasonic and triaxial compression tests) to obtain dynamic and quasi-static bulk and Young’s (elasticity) moduli. Next, rock deformation phenomena were simulated considering different parameters affecting the process. The dynamic behavior was further modeled using rock physics methods. Unlike the conventional dynamic-to-static conversion procedures, the method considers a wide range of factors affecting the relationship between the dynamic and static moduli, including strain amplitude, dispersion, rock failure mechanism, pore shape, crack parameters, poromechanics, and upscaling. A comparison between the data from laboratory and in-situ tests and the estimation results indicated promising findings. The accuracy of the results was assessed by the analysis of variance (ANOVA). In addition to modeling the static moduli, DTS can be used to verify the static and dynamic moduli values with appropriate accuracy when core data is not available.

Integrating Rock Physics, Seismic Reservoir Characterization and Static Modeling of Carbonates; a Case Study from the UAE

2010 ◽  
Author(s):  
Fathy El-Wazeer ◽  
Antonio Vizamora ◽  
Aysha Al Hamedi ◽  
Habeeba Al-Housani ◽  
Peter Abram ◽  
...  
Keyword(s):  
Reservoir Characterization ◽  
Rock Physics ◽  
Seismic Reservoir ◽  
Static Modeling

A Critical Review of Capacitance-Resistance Models

2021 ◽  
Author(s):  
Ahmed Alghamdi ◽  
Moaz Hiba ◽  
Moustafa Aly ◽  
Abeeb Awotunde
Keyword(s):  
Critical Review ◽  
Continuity Equation ◽  
Reservoir Characterization ◽  
Water Injection ◽  
Main Hypothesis ◽  
Reservoir Performance ◽  
Water Breakthrough ◽  
Powerful Analytical Tool ◽  
Fluid Properties ◽  
Injection Rates

Abstract A Capacitance Resistance Model (CRM) is an analytical model that only requires production and injection rates to predict reservoir performance. The CRM input is the injection rates and the output is the production rate. The input and output are related by the CRM parameters. The first parameter is the time delay (also called time constant) and is a function of pore volume, total compressibility, and productivity indices. The second parameter is the connectivity (also called gain, or weight), which quantifies the connectivity between producers and injectors (i.e. how much of the input is supporting the output). The CRM was developed for fields with minimum reservoir data, or for small fields not requiring a full reservoir simulation model, which can be time-consuming and expensive. The CRM is a quick, powerful analytical tool that is simple to use and requires readily available data. Most of the time, the injection and production rates are measured accurately and frequently, either weekly or bi-weekly. By solving the continuity equation for a homogenous reservoir (i.e. constant reservoir and fluid properties throughout the reservoir) the solution of the continuity equation can be indicative of the injection and production relation and therefore can be used to optimize injection schemes for higher ultimate hydrocarbon recovery. It is important to recognize that the CRM is not supposed to replace numerical reservoir simulators, which, in essence, are the most accurate means of reservoir performance prediction. Instead, the CRM aims to be a quick and easy way to infer reservoir performance in the absence of full-fledged simulation. The CRM has been used for several purposes as seen in the literature. First, as a tool to optimize waterflooding in oil reservoirs. The CRM can infer inter-well connectivity which will allow the engineer to adjust water injection rates to ensure uniform sweep in the reservoir and reduce the chance of early water breakthrough. The CRM was also used to optimize CO2 sequestration, whereby CO¬2 is captured from the atmosphere and stored in subsurface formations. The main hypothesis in CRM is that the characteristics of the reservoir can be inferred from analyzing production and injection data only. CRM does not require core data, logs, seismic, or any rock or fluids properties. This hypothesis can be challenged easily since most reservoirs have gradients of fluid properties, multi-porosity systems, and heterogeneous formations with different wettability presences. Albeit, several publications have shown that CRM can result in high certainty output. The objective of this report is to explain the concept of the CRM, conduct a critical review of the main CRM publications, compare CRM to other reservoir characterization tools and finally demonstrate some applications of the CRM.

Application of 3D static modeling for optimal reservoir characterization

2019 ◽  
Vol 152 ◽  
pp. 184-196 ◽  
Author(s):  
Adewunmi O. Adelu ◽  
A.A. Aderemi ◽  
Adesoji O. Akanji ◽  
Oluseun A. Sanuade ◽  
SanLinn I. Kaka ◽  
...  
Keyword(s):  
Reservoir Characterization ◽  
Static Modeling

scholarly journals EVALUATION OF AKPET GT9 GAS CONDENSATE RESERVOIR PERFORMANCE

2020 ◽  
Vol 1 (1) ◽  
pp. 1-13
Author(s):  
S. Okotie ◽  
N. O. Ogbarode
Keyword(s):  
Reservoir Characterization ◽  
Material Balance ◽  
Gas Condensate ◽  
Petroleum Engineering ◽  
Analysis Tool ◽  
Reservoir Performance ◽  
Future Production ◽  
Condensate Reservoir ◽  
Balance Analysis ◽  
Amount Of Knowledge

To effectively evaluate a gas condensate reservoir performance, the reservoir engineer must have a reasonable amount of knowledge about the reservoir to adequately analyze the reservoir performance and predict future production under various modes of operation. Due to the multiphase flow that exists in the reservoir, characterization of gas condensate reservoirs is often a difficult task with the variation of its overall composition in both space and time during production which complicates well deliverability analysis and the sizing of surface facilities. This study is primarily concern with the evaluation of a gas condensate reservoir performance of Akpet GT 9 Reservoir in the Niger Delta region of Nigeria with material balance analysis tool “MBal” without having to run numerical simulations. The result obtained with MBal on the analysis of Akpet GT 9 reservoir gave 23.934 Bscf of gas initially in place which compares favorably with the volume obtained from volumetric techniques. Results also shows that the most likely aquifer model is the Hurst–Van Everdingen - Dake radial aquifer and the reservoir is supported by a combined drive of water influx and fluid expansion. Okotie, S. | Department of Petroleum Engineering, Federal University of Petroleum Resources (FUPRE), Effurun, Delta State, Nigeria.

Sign in / Sign up

Export Citation Format

Share Document