Eagle Ford Huff-and-Puff Gas Injection Pilot: Comparison of Reservoir Simulation, Material Balance and Real Performance of the Pilot Well

2018 ◽  
Author(s):  
Daniel Orozco ◽  
Alfonso Fragoso ◽  
Karthik Selvan ◽  
Roberto Aguilera
Keyword(s):  
Reservoir Simulation ◽  
Gas Injection ◽  
Material Balance ◽  
Eagle Ford ◽  
Real Performance

Eagle Ford Huff ‘n’ Puff Gas-Injection Pilot: Comparison of Reservoir-Simulation, Material Balance, and Real Performance of the Pilot Well

2020 ◽  
Vol 23 (01) ◽  
pp. 247-260 ◽  
Author(s):  
Daniel Orozco ◽  
Alfonso Fragoso ◽  
Karthik Selvan ◽  
Graham Noble ◽  
Roberto Aguilera
Keyword(s):  
Reservoir Simulation ◽  
Gas Injection ◽  
Material Balance ◽  
Eagle Ford ◽  
Real Performance

Use of Reservoir Simulation to Forecast Field EOR Response - An Eagle Ford Gas Injection Huff-N-Puff Application

2020 ◽  
Author(s):  
Erich Kerr ◽  
Kiran Kumar Venepalli ◽  
K Patel ◽  
Raymond Ambrose ◽  
James Erdle
Keyword(s):  
Reservoir Simulation ◽  
Gas Injection ◽  
Eagle Ford ◽  
Forecast Field

Reservoir Simulation of Variable Bubble-Point Problems

1976 ◽  
Vol 16 (01) ◽  
pp. 10-16 ◽  
Author(s):  
L.K. Thomas ◽  
W.B. Lumpkin ◽  
G.M. Reheis
Keyword(s):  
Reservoir Simulation ◽  
Water Injection ◽  
Material Balance ◽  
Point Problem ◽  
Time Step ◽  
Bubble Point ◽  
Free Gas ◽  
Flow Equations ◽  
Gas Saturation ◽  
Surrounding Areas

Abstract This paper presents the development of a general beta reservoir simulator that will model conventional (fixed bubble-point) problems as well as problems involving a variable bubble point, such as gas injection projects above the original bubble point and water injection projects resulting in a collapsing gas saturation, Provisions are included for allowing the pressure to cross the bubble point with the same relative ease as in a conventional simulator. Example problems are presented to demonstrate the utility of the model for gas and water injection problems. problems Introduction Many reservoir simulation problems involve treating a variable bubble point throughout the reservoir. For example, when gas is injected into an undersaturated reservoir, gas will go into solution, increasing the bubble point of the oil. As this oil moves away from the injector, the bubble point of surrounding areas also may increase point of surrounding areas also may increase because of mixing. During waterfloods of saturated reservoirs, the gas saturations in regions near the injectors frequently reduce to zero at pressures below the original bubble point. Thus, the bubble point will vary areally throughout the field. point will vary areally throughout the field.Recent publications have discussed certain aspects of the variable bubble-point problem. Most of these papers contain only a brief discussion of this problem. Ridings discusses the need for allowing saturation pressure to vary continuously throughout the reservoir as long as there is free gas associated with the oil. In the model presented by Cook et al., free gas saturation is monitored for saturated systems and the bubble point is set equal to the prevailing reservoir pressure when the gas saturation in a cell disappears. Bubble points for undersaturated cells are allowed to change because of the entrance of free gas and mixing. Spilletta et al. assume that a cell that is saturated or undersaturated at the beginning of a time step will remain so throughout the time step. They then solve their saturation equations for water and gas saturations. The bubble point of any undersaturated cell is adjusted to account for nonzero gas saturation, and the water saturation is modified to conserve oil. Steffensen and Sheffield devote their paper to the reservoir simulation of a collapsing gas saturation during waterflooding. In their model, blocks that have a free gas saturation at the beginning of a time step and have zero or negative gas saturations at the end of a time step are detected, and the bubble points for these cells are set equal to the estimated pressure where Sg reduced to zero. Gas saturation for these blocks is set equal to zero and S is set to 1 - S . The oil saturation in adjacent saturated grid blocks is then adjusted so that oil material balance is maintained. Mixing caused by flow between undersaturated blocks is neglected. This paper presents a comprehensive analysis of modeling variable bubble-point problems.* It treats the specific problems of simulating gas injection above the bubble point as well as waterflooding depleted reservoirs. It differs from previously reported work by accounting for the effect of bubble-point change on computed pressure change during a time step. Also, provisions are included that allow the pressure to cross the bubble point with the same relative ease as in a conventions! simulator In regard to waterflooding, mis paper differs from the work of Steffensen and Sheffield in mat it allows for bubble-point changes caused by mixing. DEVELOPMENT OF FLOW EQUATIONS To simulate the variable bubble-point problem, the expansion of the flow equations above the bubble point must include the effects of pressure and bubble point on fluid properties. Also, special consideration must be given to cells passing through the bubble point if both pressure and material-balance errors are to be eliminated. SPEJ P. 10

scholarly journals Using Different Methods to Predict Oil in Place in Mishrif Formation / Amara Oil Field

2020 ◽  
Vol 21 (1) ◽  
pp. 33-38
Author(s):  
Mohammad Najeeb ◽  
Fadhil Sarhan Kadhim ◽  
Ghazwan Noori Saed
Keyword(s):  
Reservoir Simulation ◽  
Material Balance ◽  
Volumetric Method ◽  
Oil Field ◽  
Percentage Error ◽  
Reserve Estimation ◽  
Volume Calculation ◽  
Monte Carlo Simulation Technique ◽  
Hydrocarbon Content ◽  
Decline Curve

The reserve estimation process is continuous during the life of the field due to risk and inaccuracy that are considered an endemic problem thereby must be studied. Furthermore, the truth and properly defined hydrocarbon content can be identified just only at the field depletion. As a result, reserve estimation challenge is a function of time and available data. Reserve estimation can be divided into five types: analogy, volumetric, decline curve analysis, material balance and reservoir simulation, each of them differs from another to the kind of data required. The choice of the suitable and appropriate method relies on reservoir maturity, heterogeneity in the reservoir and data acquisition required. In this research, three types of reserve estimation used for the Mishrif formation / Amara oil field volumetric approach in mathematic formula (deterministic side) and Monte Carlo Simulation technique (probabilistic side), material balance equation identified by MBAL software and reservoir simulation adopted by  Petrel software geological model.  The results from these three methods were applied by the volumetric method in the deterministic side equal to (2.25 MMMSTB) and probabilistic side equal to (1.24, 2.22, 3.55) MMMSTB P90, P50, P10 respectively. OOIP was determined by MBAL software equal to (2.82 MMMSTB). Finally, the volume calculation of OOIP by using the petrel static model was (1.92 MMMSTB). The percentage error between material balance and the volumetric equation was equal to 20% while the percentage error between the volumetric method and petrel software was 17%.

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