Network Modeling Reservoir Rock Permeability Damage Due to Water Injection With Suspended Particles

2002 ◽  
Author(s):  
A.G. Siqueira ◽  
F.S. Shecaira ◽  
F.J. Bonet
Keyword(s):  
Network Modeling ◽  
Water Injection ◽  
Suspended Particles ◽  
Reservoir Rock ◽  
Rock Permeability ◽  
Permeability Damage

Improve Reservoir Rock Permeability Using Laser Technology - A New Advanced Approach for Increased Oil Production Rate

KazGeo 2012 ◽  
2012 ◽  
Author(s):  
M. Bakhtbidar ◽  
G.H. Montazeri ◽  
M. Alimohammadi ◽  
M.H. Bakhtbidar ◽  
M.R. Kazemi Asfeh
Keyword(s):  
Production Rate ◽  
Oil Production ◽  
Reservoir Rock ◽  
Rock Permeability ◽  
Laser Technology

scholarly journals PREDIKSI PERMEABILITAS MENGGUNAKAN METODE LOG DAN PORE GEOMETRY STRUCTURE (PGS) PADA DAERAH CEKUNGAN JAWA BARAT UTARA

2020 ◽  
Vol 6 (1) ◽  
pp. 3-17
Author(s):  
Ayu Yuliani ◽  
Ordas Dewanto ◽  
Karyanto Karyanto ◽  
Ade Yogi
Keyword(s):  
Water Saturation ◽  
Effective Porosity ◽  
Reservoir Rock ◽  
Rock Properties ◽  
Pore Geometry ◽  
Rock Permeability ◽  
Zone Formation ◽  
Geometry Structure ◽  
Rock Typing

Determination of reservoir rock properties is very important to be able to understand the reservoir better. One of these rock properties is permeability. Permeability is the ability of a rock to pass fluid. In this study, the calculation of permeability was carried out using log and PGS (Pore Geometry Structure) methods based on core data, logs, and CT scans. In the log method, the calculation of permeability is done by petrophysical analysis which aims to evaluate the target zone formation in the form of calculation of the distribution of shale content (effective volume), effective porosity, water saturation, and permeability. Next, the determination of porosity values from CT Scan. Performed on 2 data cores of 20 tubes, each tube was plotted as many as 15 points. The output of this stage is the CT Porosity value that will be used for the distribution of predictions of PGS permeability values. In the PGS method, rock typing is based on geological descriptions, then calculation of permeability predictions. Using these two methods, permeability can be calculated in the study area. The results of log and PGS permeability calculations that show good correlation are the results of calculation of PGS permeability. It can be seen from the data from the calculation of PGS permeability approaching a gradient of one value with R2 of 0.906, it will increasingly approach the core rock permeability value. Whereas the log permeability calculation for core rock permeability is 0.845.

scholarly journals Dynamics of water injection in an oil-wet reservoir rock at subsurface conditions: Invasion patterns and pore-filling events

2020 ◽  
Vol 102 (2) ◽  
Author(s):  
Abdulla Alhosani ◽  
Alessio Scanziani ◽  
Qingyang Lin ◽  
Sajjad Foroughi ◽  
Amer M. Alhammadi ◽  
...  
Keyword(s):  
Water Injection ◽  
Reservoir Rock ◽  
Pore Filling ◽  
Invasion Patterns

An Independent Analysis of the Performance Characteristics and Economic Outcomes of the Liza Phase 1 Development Offshore Guyana Using Public Domain Data

2021 ◽  
Author(s):  
Simon Paul ◽  
Kadija Dyall ◽  
Quinn Gabriel
Keyword(s):  
Oil Recovery ◽  
Dynamic Models ◽  
Computer Modelling ◽  
Phase 1 ◽  
Water Injection ◽  
Public Domain ◽  
Gas Production ◽  
Google Earth ◽  
Reservoir Rock ◽  
Original Oil In Place

Abstract An attempt was made to independently verify the proposed performance of the Liza 1 field using only data available in the public domain. The data used in modelling was sourced from news reports, company disclosures and the analogue Jubilee field in Ghana. Reservoir rock and fluid data from Jubilee Field was deemed an appropriate fit because of the corroboration provided by the Atlantic Drift Theory. A major challenge in creating the model, was determining the aerial extent of the field. According to Yang and Escalona (2011), the subsurface can be reasonably approximated using the surface topography which is possible via the use of GIS software. Google Earth Pro software was used to estimate the coordinates and areal extent of the Liza 1 reservoir. A scaled image of the field location showing the Guyana coastline was re-sized to fit the coastline in Google Pro and then the coordinates for the Liza field and wildcat well locations were estimated. This was used to create the isopach map and set reservoir boundaries to create the static and dynamic models in Schlumberger's Petrel E & P Software Platform (2017) and Computer Modelling Group IMEX Black Oil and Unconventional Simulator CMG IMEX (2016). The initialized model investigated the reservoir performance with and without pressure maintenance over a twenty (20) year period. The original oil in place (OOIP) estimated by the model was 7% larger than the OOIP estimated by ExxonMobil for Liza field. The model produced 35% of the OOIP compared to 50% of OOIP as forecasted by the operators. (See Table 1). The factors that strongly influenced this outcome were, the well positioning and the water injection rates. A significant percentage of the oil remained unproduced in the lower layers of the model after the 20-year period. Time did not permit further modelling to improve the performance of the model. Table 1 Comparison of The Created Model and ExxonMobil's Proposal for Liza. Property ExxonMobil's statement on Liza field Modelled field Result Original Oil in Place (MMbbl) 896 967 Oil Recovery Factor (%) 50 35 Gas production from the model would be used as gas injection from three injector wells and as fuel for the proposed 200 MW power plant for Guyana. Even so, significant volumes of natural gas remained unallocated and subsequently a valuable resource may have to be flared.

A New Approach to Simulation of Wax Precipitation During Cold Water Injection in Carbonate Reservoir of Kharyaga Oilfield

2021 ◽  
Author(s):  
Yuri Mikhailovich Trushin ◽  
Anton Sergeevich Aleshchenko ◽  
Oleg Nikolaevich Zoshchenko ◽  
Mark Suleimanovich Arsamakov ◽  
Ivan Vasilevich Tkachev ◽  
...  
Keyword(s):  
Cold Water ◽  
Water Injection ◽  
Carbonate Reservoir ◽  
Hot Water ◽  
Reservoir Rock ◽  
Laboratory Studies ◽  
Wax Deposition ◽  
Field Development ◽  
Sector Model ◽  
The Impact

Abstract The paper describes a methodology for assessing the impact of wax deposition in reservoir oil during cold water injection into heterogeneous carbonate reservoir D3-III of the Kharyaga field. The main goal is to determine the optimal amount of hot water that must be injected before switching to cold water without affecting the field development. The paper presents the results of laboratory studies to determine the thermophysical properties of oil, samples of net reservoir and non-reservoir rock, as well as the results of laboratory studies to determine the conditions and nature of wax deposition in oil when the temperature and pressure conditions change. Calculations were carried out to describe the physical model of oil displacement by water of various temperatures. A series of synthetic sector model runs was performed, which includes the average properties of the selected reservoir and the results of laboratory studies in order to determine the effect of cold water injection on the development performance.

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