DEFINITION OF PETROLEUM RESERVES USING PROBABILITY ANALYSIS

1979 ◽  
Vol 19 (1) ◽  
pp. 197
Author(s):  
B.G. McKay ◽  
N.F. Taylor
Keyword(s):  
Monte Carlo ◽  
Probability Distribution ◽  
Oil And Gas ◽  
Water Saturation ◽  
Cumulative Probability ◽  
Reservoir Properties ◽  
Simple Method ◽  
Volume Porosity ◽  
Cumulative Curve ◽  
Definition Of

The realistic estimation of reserves and resources is important to many diverse groups including explorers, producers, auditors, taxmen, bankers, shareholders and governments. Reserves data are used in different ways for a variety of reasons and often the figures are used without adequate definition and/or recognition of the uncertainties associated with them. Any calculation method which fails to consider the uncertainties involved, cannot portray a realistic assessment of reserves.Esso Australia Ltd. uses a relatively simple method to generate probability distribution curves in order to allow a more perceptive definition of the range of reserves for the offshore oil and gas fields in the Gippsland Basin and Esso is advocating wider petroleum and mineral industry acceptance of this approach.The method involves defining data distributions for each of the reservoir properties (volume, porosity, water saturation, compressibility and recovery factor) which are multiplied using Monte Carlo Simulation to generate the distribution of reserves. Actual input consists of data from:A high confidence area immediately surrounding well control, where the rock volume is relatively closely defined and the distributions of the other parameters, with the exception of recovery factors, reflect the observed variations.Other areas which are only seismically controlled, where the data ranges reflect both observed and interpreted variations in volume (gross and net), porosity, water saturation, compressibility and recovery factor.The curves generated for each area are then added by Monte Carlo Summation to yield the probability distribution of reserves for the whole field. In this method all available data are used and fewer subjective decisions are necessary. The computer generated distribution curves plot cumulative probability on the y-axis versus reserves on the x-axis. The curves allow the evaluation of the entire range of potential reserves, are valuable in economic and risk assessments and allow for more consistency in defining reserves for reporting purposes. The different categories of reserves, viz. "proved", "probable" or "possible", can be specified from the total field curves at defined probabilities. Moreover, the slope of the cumulative curve provides a direct indication of the level of knowledge of the field or parts of it.

scholarly journals Petrophysics Analysis for Reservoir Characterization of Upper Plover Formation in the Field “A”, Bonaparte Basin, Offshore Timor, Maluku, Indonesia

2016 ◽  
Vol 1 (1) ◽  
pp. 43 ◽  
Author(s):  
Sugeng Sapto Surjono ◽  
Indra Arifianto
Keyword(s):  
Reservoir Characterization ◽  
Water Saturation ◽  
Reservoir Rock ◽  
Reservoir Properties ◽  
Core Data ◽  
Class A ◽  
Rock Types ◽  
Rock Typing ◽  
Volume Porosity ◽  
Potential Reservoir

Hydrocarbon potential within Upper Plover Formation in the Field “A” has not been produced due to unclear in understanding of reservoir problem. This formation consists of heterogeneous reservoir rock with their own physical characteristics. Reservoir characterization has been done by applying rock typing (RT) method utilizing wireline logs data to obtain reservoir properties including clay volume, porosity, water saturation, and permeability. Rock types are classified on the basis of porosity and permeability distribution from routines core analysis (RCAL) data. Meanwhile, conventional core data is utilized to depositional environment interpretations. This study also applied neural network methods to rock types analyze for intervals reservoir without core data. The Upper Plover Formation in the study area indicates potential reservoir distributes into 7 parasequences. Their were deposited during transgressive systems in coastal environments (foreshore - offshore) with coarsening upward pattern during Middle to Late Jurassic. The porosity of reservoir ranges from 1–19 % and permeability varies from 0.01 mD to 1300 mD. Based on the facies association and its physical properties from rock typing analysis, the reservoir within Upper Plover Formation can be grouped into 4 reservoir class: Class A (Excellent), Class B (Good), Class C (Poor), and Class D (Very Poor). For further analysis, only class A-C are considered as potential reservoir, and the remain is neglected.

scholarly journals Analysing the sensitivity of input parameters for oil reserve estimation of DQ oil field in conjunction with Monte Carlo simulations

2015 ◽  
Vol 18 (2) ◽  
pp. 5-23
Author(s):  
Xuan Van Tran ◽  
Ngoc Ba Thai
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Oil And Gas ◽  
Water Saturation ◽  
Input Parameter ◽  
Simulation Software ◽  
Oil Field ◽  
Monte Carlo Algorithm ◽  
Reserve Estimation ◽  
Input Parameters

The Monte Carlo algorithm is used widely in the areas of humanlife, such as currency risk calculations, mathematical probability and statistics, atmospheric research, materials research applications in laser ... In the oil and gas sector, the Monte Carlo algorithm is mostly applied in oil and gas exploration. Worldwide there are many researchs worked on the Monte Carlo algorithm application through oil and gas reserve estimation. In Vietnam, the reserve estimation with the support of simulation software is no wonder, particularly Monte Carlo algorithms have been adopted on the reserve estimation for many years. However, this algorithm is just applied to predict results. The analysis of the influence of each input parameter on the calculation for reserve estimation is quite restricted. Therefore the article refers to the sensitivity analysis of each input parameter for oil reserve estimation of DQ oil field in conjunction with Monte Carlo simulations in the territory of Vietnam in order to improve reliability of the results. Analyzing results the effects of the input parameters to the reserve estimation by volumetric methods in DQ oil field shows there are five effect parameters (Bulk rock volume, initial water saturation, porosity, formation volume factors, the net to gross thickness ratio), porosity which influence range varies from 0.66 ÷ 0.83 is the greatest impact factor to the assessment results.

scholarly journals ON THE POSSIBILITY OF WETTABILITY ASSESSMENT OF OIL AND GAS RESERVOIRS BY THEIR MAIN COLLECTOR PROPERTIES

2020 ◽  
Keyword(s):  
Film Thickness ◽  
Oil And Gas ◽  
Water Saturation ◽  
Core Material ◽  
Residual Water ◽  
Laboratory Studies ◽  
Practical Application ◽  
Reservoir Properties ◽  
Standard Methods ◽  
Water Holding

Problem statement. In some publications, the possibility of determining the wettability according to geophysical studies of wells (GIS), in particular, defined in the complex GIS residual water saturation or water retention capacity. As the main quantitative indicator of wettability, the thickness of the fictitious film of residual water is used. If this idea is true, then the calculation of wettability is possible and the results of determining the same parameters in the course of laboratory studies of core material. The purpose of this work is to test the proposed method for calculating wettability on the basis of data on the main reservoir properties of rocks obtained in the laboratory and to assess the possibility of practical application of this technique. Scientific and practical significance. The wettability of the rock surface is an important parameter on which the main indicators of the development of hydrocarbon deposits depend. At the moment, many oil and gas companies are experiencing difficulties in developing long-term fields. This is a breakthrough of water during water flooding, selective flooding of the wells and increased water-repellency of the reservoir in the development process. Taken together, this leads to a decrease in the rate of hydrocarbon extraction, a significant increase in flooding and, as a result, to a decrease in the final indicators of hydrocarbon recovery and significant economic losses. There are many methods of influencing the oil and gas reservoir in order to obtain a cost-effective inflow of hydrocarbons. But whatever method was used, there is a question of control and adjustment of wettability, the solution of which is impossible without determining the real relative wettability of the reservoir with water and hydrocarbons. Core material, which can determine the wettability of standard methods, is not selected enough and the possibility of calculating the wettability of GIS data could fundamentally improve the situation. Analysis of available publications on the topic. In the course of the work, the theoretical background of the proposed technique, the conclusion of the formula for calculating the thickness of the dummy film and the data on practical application given in the available works were analyzed. According to the authors of the tested method, the degree of hydrophilicity of the productive formation is qualitatively characterized by the content of residual water: the higher its content, the more hydrophilic the rock. To quantify the degree of hydrophilicity, it is proposed to use the thickness of the fictitious film of residual water on the surface of pore channels, which should increase in direct proportion to the degree of hydrophilicity and which is determined by the values of the water-holding capacity of rocks. Materials of own researches. On the basis of laboratory studies, the results of which are presented in the article, it can be argued that the values of residual water saturation with increasing hydrophilicity can both increase and decrease. The relationship between residual water saturation and wetting angle is ambiguous and should be used with great care to assess the degree of hydrophilicity of rocks. Quantification of hydrophilicity on the thickness of the dummy film of residual water is complicated by the fact that, first, to determine the film thickness for water-holding capacity is highly problematic, they are too weakly bound, and secondly, the film thickness is poorly and ambiguously connected with the wetting, which used the wetting angle, and the index "M" defined by standard methods. Perhaps it is the proposed formulas for calculating the thickness of the fictitious film and the idea can be further developed, however, at this stage to replace the direct definition of wettability on the calculation of known values of reservoir properties is impossible. Conclusion. Calculation of wettability by known values of basic reservoir properties determined in laboratory conditions is impossible. We stipulate that this conclusion cannot be unconditionally transferred to the assessment of wettability according to GIS (determination of both residual water saturation and gas saturation coefficient according to GIS has its own specifics), but the impossibility of constructing the desired connections in the laboratory forces caution to approach such calculations. The performed work will help to avoid gross errors in the assessment of wettability, performed for various practical purposes, in particular, in the development of methods to prevent selective flooding of wells.

Application of Acoustic Impedance and Reflectivity in Reservoir Characterization

2021 ◽  
Author(s):  
Stanley Oifoghe ◽  
Ikenna Obodozie ◽  
Lucrecia Grigoletto
Keyword(s):  
Acoustic Impedance ◽  
Oil And Gas ◽  
Reservoir Characterization ◽  
Water Saturation ◽  
Oil And Gas Industry ◽  
Well Log ◽  
Volume Calculation ◽  
Geomechanical Properties ◽  
Volume Porosity ◽  
Well Log Analysis

Abstract Well log analysis is one of the methods for reservoir characterization, in the oil and gas industry. Logs are used for subsurface formation evaluation. They are useful in hydrocarbon zone identification and volume calculation. Interpretation of well log involves sequential steps, which are lithology, shale volume, porosity and saturation determination. It is unwise to analyze well log without following the logical steps, as this could introduce errors in the result. Petrophysical and Geomechanical properties are two classes of properties for reservoir characterization. The computed volume of shale in the reservoir was 10%, the average water saturation was 30%, and the average porosity was 25pu. The bulk density decreased from 2.15g/cc to 1.95g/cc and there is a considerably lower acoustic impedance in the hydrocarbon bearing sands. In challenging reservoirs, where traditional petrophysical methods do not give definitive results, the use of geomechanical methods will improve interpretation certainty and help to clear doubts in the interpreted results.

Deep fluids and their role in hydrocarbon migration and oil deposit formation exemplified by supercritical СO2

2021 ◽  
pp. 1-11
Author(s):  
Sara LIFSHITS
Keyword(s):  
Supercritical Fluid ◽  
Oil And Gas ◽  
Gas Permeability ◽  
Sedimentary Rocks ◽  
Source Rocks ◽  
Reservoir Properties ◽  
Hydrocarbon Migration ◽  
Geological Processes ◽  
Before And After ◽  
Oil Source

ABSTRACT Hydrocarbon migration mechanism into a reservoir is one of the most controversial in oil and gas geology. The research aimed to study the effect of supercritical carbon dioxide (СО2) on the permeability of sedimentary rocks (carbonates, argillite, oil shale), which was assessed by the yield of chloroform extracts and gas permeability (carbonate, argillite) before and after the treatment of rocks with supercritical СО2. An increase in the permeability of dense potentially oil-source rocks has been noted, which is explained by the dissolution of carbonates to bicarbonates due to the high chemical activity of supercritical СО2 and water dissolved in it. Similarly, in geological processes, the introduction of deep supercritical fluid into sedimentary rocks can increase the permeability and, possibly, the porosity of rocks, which will facilitate the primary migration of hydrocarbons and improve the reservoir properties of the rocks. The considered mechanism of hydrocarbon migration in the flow of deep supercritical fluid makes it possible to revise the time and duration of the formation of gas–oil deposits decreasingly, as well as to explain features in the formation of various sources of hydrocarbons and observed inflow of oil into operating and exhausted wells.

scholarly journals AB0140 A HIGH-CONFIDENCE DEFINITION OF THERAPEUTIC RESPONSE IN RHEUMATOID ARTHRITIS USING A MONTE CARLO SIMULATION APPROACH

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 1097.2-1098
Author(s):  
V. Strand ◽  
S. Cohen ◽  
L. Zhang ◽  
T. Mellors ◽  
A. Jones ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Monte Carlo ◽  
Disease Activity ◽  
Health Assessment Questionnaire ◽  
Health Assessment ◽  
High Confidence ◽  
Response Status ◽  
Definition Of ◽  
Fidelity Score ◽  
Assessment Questionnaire

Background:Therapy choice and therapy change depend on the ability to accurately assess patients’ disease activity. The clinical assessments used to evaluate treatment response in rheumatoid arthritis have inherent variability, normally considered as measurement error, intra-observer variability or within subject variability. Each contribute to variability in deriving response status as defined by composite measures such as the ACR or EULAR criteria, particularly when a one-time observed measurement lies near the boundary defining response or non-response. To select an optimal therapeutic strategy in the burgeoning age of precision medicine in rheumatology, achieve the lowest disease activity and maximize long-term health outcomes for each patient, improved treatment response definitions are needed.Objectives:Develop a high-confidence definition of treatment response and non-response in rheumatoid arthritis that exceeds the expected variability of subcomponents in the composite response criteria.Methods:A Monte Carlo simulation approach was used to assess ACR50 and EULAR response outcomes in 100 rheumatoid arthritis patients who had been treated for 6 months with a TNF inhibitor therapy. Monte Carlo simulations were run with 2000 iterations implemented with measurement variability derived for each clinical assessment: tender joint count, swollen joint count, Health Assessment Questionnaire disability index (HAQ-DI), patient pain assessment, patient global assessment, physician global assessment, serum C-reactive protein level (CRP) and disease activity score 28-joint count with CRP.1-3 Each iteration of the Monte Carlo simulation generated one outcome with a value of 0 or 1 indicating non-responder or responder, respectively.Results:A fidelity score, calculated separately for ACR50 and EULAR response, was defined as an aggregated score from 2000 iterations reported as a fraction that ranges from 0 to 1. The fidelity score depicted a spectrum of response covering strong non-responders, inconclusive statuses and strong responders. A fidelity score around 0.5 typified a response status with extreme variability and inconclusive clinical response to treatment. High-fidelity scores were defined as >0.7 or <0.3 for responders and non-responders, respectively, meaning that the simulated clinical response status label among all simulations agreed at least 70% of the time. High-confidence true responders were considered as those patients with high-fidelity outcomes in both ACR50 and EULAR outcomes.Conclusion:A definition of response to treatment should exceed the expected variability of the clinical assessments used in the composite measure of therapeutic response. By defining high-confidence responders and non-responders, the true impact of therapeutic efficacy can be determined, thus forging a path to development of better treatment options and advanced precision medicine tools in rheumatoid arthritis.References:[1]Cheung, P. P., Gossec, L., Mak, A. & March, L. Reliability of joint count assessment in rheumatoid arthritis: a systematic literature review. Semin Arthritis Rheum43, 721-729, doi:10.1016/j.semarthrit.2013.11.003 (2014).[2]Uhlig, T., Kvien, T. K. & Pincus, T. Test-retest reliability of disease activity core set measures and indices in rheumatoid arthritis. Ann Rheum Dis68, 972-975, doi:10.1136/ard.2008.097345 (2009).[3]Maska, L., Anderson, J. & Michaud, K. Measures of functional status and quality of life in rheumatoid arthritis: Health Assessment Questionnaire Disability Index (HAQ), Modified Health Assessment Questionnaire (MHAQ), Multidimensional Health Assessment Questionnaire (MDHAQ), Health Assessment Questionnaire II (HAQ-II), Improved Health Assessment Questionnaire (Improved HAQ), and Rheumatoid Arthritis Quality of Life (RAQoL). Arthritis Care Res (Hoboken) 63 Suppl 11, S4-13, doi:10.1002/acr.20620 (2011).Disclosure of Interests:Vibeke Strand Consultant of: Abbvie, Amgen, Arena, BMS, Boehringer Ingelheim, Celltrion, Galapagos, Genentech/Roche, Gilead, GSK, Ichnos, Inmedix, Janssen, Kiniksa, Lilly, Merck, Novartis, Pfizer, Regeneron, Samsung, Sandoz, Sanofi, Setpoint, UCB, Stanley Cohen: None declared, Lixia Zhang Shareholder of: Scipher Medicine Corporation, Employee of: Scipher Medicine Corporation, Ted Mellors Shareholder of: Scipher Medicine Corporation, Employee of: Scipher Medicine Corporation, Alex Jones Shareholder of: Scipher Medicine Corporation, Employee of: Scipher Medicine Corporation, Johanna Withers Shareholder of: Scipher Medicine Corporation, Employee of: Scipher Medicine Corporation, Viatcheslav Akmaev Shareholder of: Scipher Medicine Corporation, Employee of: Scipher Medicine Corporation

Criteria for Accrediting Expert Wine Judges

2013 ◽  
Vol 9 (1) ◽  
pp. 62-74 ◽  
Author(s):  
Robert Hodgson ◽  
Jing Cao
Keyword(s):  
Probability Distribution ◽  
Wine Industry ◽  
Ordinal Scale ◽  
Professional Experience ◽  
Wine Quality ◽  
Basic Premise ◽  
Definition Of ◽  
Jel Classifications

AbstractA test for evaluating wine judge performance is developed. The test is based on the premise that an expert wine judge will award similar scores to an identical wine. The definition of “similar” is parameterized to include varying numbers of adjacent awards on an ordinal scale, from No Award to Gold. For each index of similarity, a probability distribution is developed to determine the likelihood that a judge might pass the test by chance alone. When the test is applied to the results from a major wine competition, few judges pass the test. Of greater interest is that many judges who fail the test have vast professional experience in the wine industry. This leads to us to question the basic premise that experts are able to provide consistent evaluations in wine competitions and, hence, that wine competitions do not provide reliable recommendations of wine quality. (JEL Classifications: C02, C12, D81)

PO-1341: Monte Carlo secondary plan check: validation and definition of the action limits for patient QA

2020 ◽  
Vol 152 ◽  
pp. S709-S710
Author(s):  
I. Fotina ◽  
S. Siamkousky ◽  
A. Zverava ◽  
M. Alber
Keyword(s):  
Monte Carlo ◽  
Definition Of
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