Evaluation Uncertainty in the Volume of Oil in Place in Mishrif Reservoir
Calculating oil reserves is one of the most important applications of geological models, as it is considered an essential step to evaluate whether the reservoir is economical or not. Uncertainty methods can be used based on several reservoir factors to predict a range of reserve values, each value g...
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University of Tehran
2024-12-01
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author | Loay Khamees Farah Abdulrazzaq |
author_facet | Loay Khamees Farah Abdulrazzaq |
author_sort | Loay Khamees |
collection | DOAJ |
description | Calculating oil reserves is one of the most important applications of geological models, as it is considered an essential step to evaluate whether the reservoir is economical or not. Uncertainty methods can be used based on several reservoir factors to predict a range of reserve values, each value gives a range of production forecasts. These values are divided into probable estimates that give the highest, lowest and mean expected production, called P90, P50, and P10. Geostatistical models of the reservoirs P90, P50, and P10 must be established for dynamic models, analysis of the risk, reservoir management, and prediction. Formation volume factors, initial water saturation, and formation porosity values might be used to produce a range of values for the reserve via the volumetric method. A reserve requires to be proven when there is a probability of 90% indicating that the recovered quantities in reality are equal or above the estimates. These are typically denoted as P90 throughout the estimating process. P10 refers to the total of potential and probable reserves, and P50 refers to proven and probable reserves. In this research, these quantities were calculated using statistical functions to assess the uncertainty in the oil volume. This was done by building a geological model from the data of a group of wells using the Petrel program. Then the uncertainty techniques were used to determine the expected values of the uncertain variables and their corresponding values of oil in place originally (OOIP). The result of OOIP values presents that the OWC level is the most influential parameter on oil in place. A histogram was created with bin values ranging from 3300 to 3700 and with Bin step equal to 25 and the normal distribution for these bins was calculated to estimate P10, P50, and P90 values. |
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institution | Kabale University |
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language | English |
publishDate | 2024-12-01 |
publisher | University of Tehran |
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spelling | doaj-art-5d9b14e627004c2c852019e96b3bc7982025-01-21T14:29:48ZengUniversity of TehranJournal of Chemical and Petroleum Engineering2423-673X2423-67212024-12-0158224325410.22059/jchpe.2024.373776.149196498Evaluation Uncertainty in the Volume of Oil in Place in Mishrif ReservoirLoay Khamees0Farah Abdulrazzaq1Department of Petroleum Refining engineering, College of Petroleum Processes Engineering, Tikrit university, Tikrit, IraqDepartment of petroleum Engineering, College of Engineering, Basrah University, BasrahCalculating oil reserves is one of the most important applications of geological models, as it is considered an essential step to evaluate whether the reservoir is economical or not. Uncertainty methods can be used based on several reservoir factors to predict a range of reserve values, each value gives a range of production forecasts. These values are divided into probable estimates that give the highest, lowest and mean expected production, called P90, P50, and P10. Geostatistical models of the reservoirs P90, P50, and P10 must be established for dynamic models, analysis of the risk, reservoir management, and prediction. Formation volume factors, initial water saturation, and formation porosity values might be used to produce a range of values for the reserve via the volumetric method. A reserve requires to be proven when there is a probability of 90% indicating that the recovered quantities in reality are equal or above the estimates. These are typically denoted as P90 throughout the estimating process. P10 refers to the total of potential and probable reserves, and P50 refers to proven and probable reserves. In this research, these quantities were calculated using statistical functions to assess the uncertainty in the oil volume. This was done by building a geological model from the data of a group of wells using the Petrel program. Then the uncertainty techniques were used to determine the expected values of the uncertain variables and their corresponding values of oil in place originally (OOIP). The result of OOIP values presents that the OWC level is the most influential parameter on oil in place. A histogram was created with bin values ranging from 3300 to 3700 and with Bin step equal to 25 and the normal distribution for these bins was calculated to estimate P10, P50, and P90 values.https://jchpe.ut.ac.ir/article_96498_ee08df1f7fe35bcc765363d2f4a3d7e4.pdfgeostatistical modelsmishrif reservoirpetrel programoil volumeooip |
spellingShingle | Loay Khamees Farah Abdulrazzaq Evaluation Uncertainty in the Volume of Oil in Place in Mishrif Reservoir Journal of Chemical and Petroleum Engineering geostatistical models mishrif reservoir petrel program oil volume ooip |
title | Evaluation Uncertainty in the Volume of Oil in Place in Mishrif Reservoir |
title_full | Evaluation Uncertainty in the Volume of Oil in Place in Mishrif Reservoir |
title_fullStr | Evaluation Uncertainty in the Volume of Oil in Place in Mishrif Reservoir |
title_full_unstemmed | Evaluation Uncertainty in the Volume of Oil in Place in Mishrif Reservoir |
title_short | Evaluation Uncertainty in the Volume of Oil in Place in Mishrif Reservoir |
title_sort | evaluation uncertainty in the volume of oil in place in mishrif reservoir |
topic | geostatistical models mishrif reservoir petrel program oil volume ooip |
url | https://jchpe.ut.ac.ir/article_96498_ee08df1f7fe35bcc765363d2f4a3d7e4.pdf |
work_keys_str_mv | AT loaykhamees evaluationuncertaintyinthevolumeofoilinplaceinmishrifreservoir AT farahabdulrazzaq evaluationuncertaintyinthevolumeofoilinplaceinmishrifreservoir |