Order from us for quality, customized work in due time of your choice.
Introduction
Estimation of oil reserves is important to the oil industry. It allows for future planning for fields, companies and countries. All these levels require a basis for assessing the value of a production through development of standards for determining volumes of reserves. By using probable, possible and proved reserve categories of probabilistic methods to produce confidence in estimates, there is the possibility of making a determination of risk and uncertainties of a reservoir (Hanif, Khan and Haneef 2012, p. 45).
The distinguishing aspect between companies revolves around the practices used by companies and their economic performances. The degree of application of probabilistic methods and their adoption in integration of multi-disciplinary processes, with aims for supporting processes for making decisions through the life cycle of assets and asset portfolios. Probable reserves are uncertain and, through the hypothesis of the philosophy of best practices, reserve estimations determine the capacity of hydrocarbon volumes and the accessibility of uncertainties in estimates (Hunt, Birch and Warne 2010, p. 1172-1181).
Uncertainty estimations of resource quantities
Uncertainty ranges are a reflection of estimation quantities for potentially recoverable amounts of gas from an accumulation with a chance of commerciality. This represents the possibility that a project developed in a project is possibly developed for the production of commercial statuses (Hong, Xiongqi, Haijun, Changsong, Qingyang and Huaijie 2010 p. 1078-1096). Geological and engineering data form the basis for determining the recoverability of oil reserve for exploitation in current economic conditions, governmental regulations and operational methods. Recoverability of unapproved reserves has a connection to technology and economic statuses.
Approved reserves determinations require that approved reserves should contain a 90% or above probability of the projected estimate quantities. In these reserves, estimations of petroleum quantities through engineering and geosciences provide estimates made with the possibilities of commercial recoverability (Manzano and Monaldi 2008, p. 59). This works through a timed period onwards from determined reservoirs within minimally defined economic statuses, governmental regulations and operating methodologies. The use of probabilistic methods requires the recovery of at least 90% of the recovered quantities or exceed of the estimates (Michel 2011, p. 434-449).
Probable reserves require a minimum probability of 50% showing that the probable reserves can provide the quantities estimated or an excess during recovery. The categorisation of these reserves using data of geosciences and engineering have minimal likeliness of recoverability than proved reserves. However, they are in possession of a probability of recoverability beyond that of possible reserves. The likeliness is that the quantities remaining for recovery exceed or are less than the sum of the estimations of Proves and possible reserves.
Possible reserves require 10% possibility as a minimum probability for required quantities to recover or an excess of the sum of the probable quantities and reserve possibilities (McLaughlin, RJ 2008 p. 1-3). These reserves as categorised by engineering and geosciences data as have little likeness of recoverability than probable reserves. The quantities recovered from these projects contain low probabilities for exceeding the sum of possible plus proved plus probable reserves with equivalence to high estimates (Zhou, Zhao, Liu, Ye, Li, Huang, Zhou and Dong 2011 p. 899-910).
To report potential results form the means making communication of the estimates and uncertainties of a reserve. To make a probabilistic estimation, it is necessary to incorporate a full range of values of all available input parameters. For the computation of the results and outcomes of recoverable quantities, there can be use of random sampling. This way is applicable for making volumetric calculations of the estimates of a reservoir in early stages of exploitation and development of projects (Zhaoming and Huashan 2010 p. 1195-1208). Categorisation guidelines of resources provide means of classifying of parameter limits necessary for all categories. Analysis of resources should as well make considerations for commercial uncertainties. Moreover, by using probabilistic methods, there is a requirement of consideration of constraints on parameters for ensuring that results are within the imposed range of deterministic and commercial uncertainties and guidelines (Zhaoming and Huashan 2010, p. 1195-1208).
Aggregation of resources of more than one hydrocarbon accumulation
Through an assessment of reservoir level, it is possible to make estimates and categorisation of gas and oil quantities. The categorisations take place in single reservoirs or in portions of the reservoirs. There is a summation of the estimates to determine the field estimates, projects and properties. Further, summation is useful in provision of totals to use in ranking of companies, areas and countries. It allows for the reporting of the estimates relevant to each level. There are differing distribution levels of these estimates in different locations of individual reporting levels given the geological setting and maturity of the assessed resources. This process of cumulative summation is aggregation (Rogner 1997, p. 217).
There are two methods of aggregation applicable in estimation of multiple resources. Both methods produce significantly divergent results in cases of application. It is practical to note a dependency between reservoirs within a single field. For that reason, probabilistic calculations require that all the dependencies form the components of calculations of estimates. In an instance of a present dependency not included or accounted for in the calculations, there is a high chance for overestimation of low estimate results and underestimation of high estimate results in probabilistic aggregation (Veli, Malvi, Cvetkovi and Vrbanac 2012, p. 91-101). In consideration of industry practice of determination of prospective resources, the chances of discovery depend on the probability that all needed components for accumulation for formation of Hydrocarbon are present. It starts with an evaluation of the potential of the quantity of the undertaken discovery. This requires a probabilistic approach for the determination of full distribution and the range of uncertainty in potentially recoverable quantities within a location of discovery.
Given the possibility of presence of certain outcomes in a range, that is below the required economic threshold, for the definition of development chance, there becomes the need for determination of the chance of commerciality through multiplication of chances of discovery. Therefore, distribution of potential outcomes requires recomputation for success discovery that can be larger than the given economic threshold (Salameh1999, p. 113-125). Through seismic estimation of resources and reserves, there is the grouping of data in structures and geometric trapping of Hydrocarbon. In trap geometry, there is a determination using dips and strikes in reservoirs and seals. There is also the determination of faults and barriers facilitating or blocking the flow of block fluid, shapes and sedimentary body distributions.
There are interpretive techniques used by geoscientists for the analysis of seismic volume. There is making of cross sections, 3D visualisations and maps for surfaces such as fault planes, bed boundaries and unconformities for the determination of the thickness of the trap. This helps in the definition of the reservoirs and adjacent ceiling units and then follows the distillation of displays to render single and multiple pools forming the field (Sokolov, et al 2009, p. 5175-5204). Through analysis of the trap, it is possible to calculate the bulk volume of the reservoir in the pools for later integration with the properties of the reservoir such as porosity, hydrocarbon saturation and net-to-gross for computation of estimates of original gas and oil in the field.
In the assessment of rock and fluid properties, the 3D seismic analysis makes a prediction of the pore-fluid and rock properties in a reservoir and at time, it determines the pressure regime of the reservoir. Under good conditions, 3D seismic can predict porosity, availability of gas/ oil, lithology, pressure and porosity. Availability of hydrocarbons lowers the density and seismic velocity of unconsolidated and moderately consolidated sandstones and this leads to modification of the contrasting shales around the water bearing sands containing the shales. A flat top represents a reflection from hydrocarbon and water contact in reservoirs where the seismic resolution is below the thickness of the reservoir. Distinguishing of gas accumulations, which are fully saturated and those that are partially saturated columns or residual gases is possible by use of conventional or full stack analysis leaving it an unresolved risk.
There can be direct estimations of contrasts in density by using higher order to analyse the difference between the two using new technologies emerging in the field. However, there is no convincing proof through seismic evidence through integrated analysis of data for the achievement of results of recoverable volumes that are under the Lowest Known Hydrocarbons (LKH) recorded from the wells. There is appraisal of hydrocarbon accumulation by use of seismic flat tops and anomalies in seismic amplitudes in the presence of conditions such as when there is visibility of flat spot and seismic anomalies without the emergence of any imaging issues in 3D seismic (Qiming, Guanghui, Xiongqi, Wenqin, Chunshu, Chenglin, Xinsheng and Bo 2010 p. 1180-1194).
A strong connection in the contact of hydrocarbon and water in a single fault block, pressure, well test, well logs and performance data with down-dip edge and flat stop of a seismic anomaly can also help in the appraisal of hydrocarbon. Flat top mapping and down dip edge mapping of the amplitude of an anomaly in a reservoir fitting in a structural contour is a usual situation of a down dip of accumulation.
It is also possible to support fluid and reservoir continuity in faulted reservoirs within the conditions of fluid data, test data, pressure and well logs demonstrating a strong tie between the seismic anomaly and hydrocarbon-bearing reservoir in a drilled fault block. Another necessary requirement is when the thickness of a reservoir is greater than a fault throw and is above the section of the fault of the hydrocarbon bearing does not stand as a major, a fault or a potential sealing. It is also necessary that the seismic anomaly and the flat spot throw stays in partial continuity within the limits of same depths across a fault (Gholami and Asadollahi 2008 p. 2484-2489). Meeting such conditions makes the presence of hydrocarbon in adjacent fault blocks on top of the seismic flat spot and seismic amplitude anomaly to qualify for a volume of hydrocarbon in similar quantities of the accumulation making the section to qualify as a reserve.
Material Balance models and/ or simple reservoir simulators
The use of material balance in estimating production of resources focuses on the pressure behavior of the withdrawal of reservoir fluids. For homogeneous and high permeability reservoir rocks with sufficient and high quality pressure, it is the ideal method for use in depletion of the gas estimates (Davydenko 2011, p. 557-562).
The process helps in estimating of reliable recovery points that allow for abandonment of pressure. In complicated cases such as those of multiphase behavior, compartmentalisation, water influx, and in multilayered permeability reservoirs, it is advisable that geologists avoid using material balance in making of estimates, as it would not produce the best results. Evaluators need to take care for the accommodation of complexities in such reservoirs and the pressure for effective responses to depletion for development of uncertainty profiles to assist in the recovery process of the project (Dongxia, Xiongqi, Jun, Dejiang, Lei and Yougen 2010 p. 1256-1272).
Through computerised models, there are sophisticated alternatives of material balance concepts known as the reservoir simulation that can be used for analysis. The capacities of the model can help in determination reservoir capacities by studying of the inputs of rock properties, permeability functions, reservoir geometry and fluid properties as critical components of consideration in estimation (Caineng, Rukai, Wenzhi, Chengzao, Guangya, Xuanjun, Xia and Baihong 2010 p. 194-205). The process can help in making recoverable quantities with sufficient production histories. Oil companies make considerations of long-term performances they can achieve in their assets and prefer making the best estimates for the volumes to use for the purpose of investment (Caineng, Rukai, Wenzhi, Chengzao, Guangya, Xuanjun, Xia and Baihong 2010 p. 194-205).
They base their estimates on proved and probable volumes working on the assumption that the portfolios of the best estimates they make are realistic and realisable in the case where the upside of one situation compensates for the downside of another situation. Probabilistic aggregation requires the consideration of field, property and project levels in the determination of summations arithmetically. Internal portfolio analysis requires that companies use probabilistic methods with application of risk possibilities where possible.
Accountants, utilities and investors need high levels of uncertainties and they make their concentration in approved volumes. For gas contracts, there is the use of proved reserves always acting as an incentive for determination of accuracy and summation for proved reserves. There is also the basing of long-term gas contracting on proved plus probable reserves with large gas resources as an economical development of gas contracts. The use of proved developed reserve by accountants forms the basis for depletion and depreciation of the cost of acquisition and development reservoirs for the produced reservoirs over time. There are occasions when the ration of production of proved plus Probable Reserves and underdeveloped reserves form the basis of depreciation. Through depreciation of the investment costs, there is the probability of determining the impact on business profits and indication of returns on average capital employed (ROACE). Such calculations make accountants assess reserves from the levels of applicable investments (Bons and Soesoo 2003, p. 412-420).
Using probabilistic summation for calculation of reservoir volumes with two independent blocks, there can be application of mean values through straightforward summation. However, the derivation of proved value for from sum distribution produces situations like in the case of Monte Carlo simulation, which produces low outcomes sections with high outcomes in some sections. Practically, the optimistic outcomes on some sections compensate for the disappointments of outcomes in certain sections. The result produces cumulative distribution for the combination of steeper GIIP. This produces a statistical phenomenon, which is observable for addition of quantities with statistical distributions that are statistical (Aigbedion 2007, p. 182-188).
Conclusion
Estimation of reserves is a process with multiple complexities from the effect of various factors. However, the process requires consideration of sound engineering and geoscientific practices to overcome the obstacles and develop reasonable estimates. Field recording histories require mixed records applicable within the industry. The mixing of strategies of developmental consistencies is relevant in the estimation of reserves. There are fields where fluctuations occur after some time and the estimation of fluctuations in estimations of reserves have cost penalties even in situations of anticipation of estimated recoveries. Multipoint approaches are necessary for the maintenance of a reliable estimation in the industry (Nixon 1999, p. 211-212). In the estimation of the ranges of uncertainty within the recoverable quantities, there is a relation of volumetric methods for the desertion of parameters and estimates of average porosity and gross rock volume. The combination of different parameters provides specific scenarios making probabilistic methods to be scenario-based. The probabilistic method uses the Monte Carlo analysis in the definition of uncertainty of distributions of parameters of input and correlations and relationships between techniques and correlations for output distributions.
References
Aigbedion, II 2007, Managing Uncertainties in Hydrocarbon-in-Place Volumes in a Northern Depobelt Field, Niger Delta, Nigeria, Journal Of Applied Sciences, vol. 7 no. 2, pp. 182-188.
Bons, P, & Soesoo, A 2003, Could Magma Transport And Accumulation Be A Useful Analogue To Understand Hydrocarbon Extraction? Oil Shale, vol. 20, pp. 412-420,
Caineng, Z, Rukai, Z, Wenzhi, Z, Chengzao, J, Guangya, Z, Xuanjun, Y, Xia, Z, & Baihong, W 2010, Geologic Characteristics of Volcanic Hydrocarbon Reservoirs and Exploration Directions in China, Acta Geologica Sinica (English Edition), vol. 84 no. 1, pp. 194-205.
Davydenko, DB 2011, Separate forecast of zonation of the oil and gas accumulation by the method of remote fluid indexation, Doklady Earth Sciences, vol. 438 no. 1, pp. 557-562.
Dongxia, C, Xiongqi, A, Jun, K, Dejiang, K, Lei, L, & Yougen, D 2010, Control of Fades and Potential on Jurassic Hydrocarbon Accumulation and Prediction of Favorable Targets in the Hinterland Region of the Junggar Basin, Acta Geologica Sinica (English Edition), vol. 84 no. 5, pp. 1256-1272.
Gholami, A, & Asadollahi, A 2008, Improving Wheat Grain Yield under Water Stress by Stem Hydrocarbon Reserve Utilization, Pakistan Journal Of Biological Sciences, vol. 11 no. 21, pp. 2484-2489.
Hanif, M, Khan, M, & Haneef, M 2012, Hydrocarbon Prospects in the Khyber Pakhtunkhwa: Future strategies, Journal Of Himalayan Earth Science, vol. 45 no. 2, p. 45.
Hong, P, Xiongqi, P, Haijun, Y, Changsong, L, Qingyang, M, & Huaijie, W 2010, Alteration and Reformation of Hydrocarbon Reservoirs and Prediction of Remaining Potential Resources in Superimposed Basins, Acta Geologica Sinica (English Edition), vol. 84 no. 5, pp. 1078-1096.
Hunt, J, Birch, G, & Warne, M 2010, Site-specific probabilistic ecological risk assessment of a volatile chlorinated hydrocarbon-contaminated tidal estuary, Environmental Toxicology & Chemistry, vol. 29 no. 5, pp. 1172-1181.
Manzano, O, & Monaldi, F 2008, The Political Economy of Oil Production in Latin America, Economia: Journal Of The Latin American And Caribbean Economic Association, vol. 9 no. 1, p. 59.
Michel, B 2011, Oil production: A probabilistic model of the Hubbert curve, Applied Stochastic Models In Business & Industry, vol. 27 no. 4, pp. 434-449.
McLaughlin, RJ 2008, Hydrocarbon Development in the Ultra-Deepwater Boundary Region of the Gulf of Mexico: Time to Reexamine a Comprehensive U.S.-Mexico Cooperation Agreement, Ocean Development & International Law, vol. 39 no. 1, pp. 1-31.
Nixon, DB 1999, Offshore Atlas Project: Methodology and Results, Marine Georesources & Geotechnology, vol. 17 no. 2/3, pp. 211-212.
Qiming, L, Guanghui, W, Xiongqi, P, Wenqin, P, Chunshu, L, Chenglin, W, Xinsheng, L, & Bo, Z 2010, Hydrocarbon Accumulation Conditions of Ordovician Carbonate in Tarim Basin, Acta Geologica Sinica (English Edition), vol. 84 no. 5, pp. 1180-1194.
Rogner, HH 1997, AN ASSESSMENT OF WORLD HYDROCARBON RESOURCES, Annual Review Of Energy & The Environment, vol. 22 no. 1, p. 217.
Salameh, MG 1999, Technology, Oil Reserve Depletion and the Myth of the Reserves-to-Production Ratio, OPEC Review, vol. 23 no. 2, pp. 113-125.
Sokolov, A, Stone, P, Forest, C, Prinn, R, Sarofim, M, Webster, M, Paltsev, S, Schlosser, C, Kicklighter, D, Dutkiewicz, S, Reilly, J, Wang, C, Felzer, B, Melillo, J, & Jacoby, H 2009, Probabilistic Forecast for Twenty-First-Century Climate Based on Uncertainties in Emissions (Without Policy) and Climate Parameters, Journal Of Climate, vol. 22 no. 19, pp. 5175-5204.
Veli, J, Malvi, T, Cvetkovi, M, & Vrbanac, B 2012, Reservoir geology, hydrocarbon reserves and production in the Croatian part of the Pannonian Basin System, Geologia Croatica, vol. 65 no. 1, pp. 91-101.
Zhaoming, W, & Huashan, L 2010, Different Hydrocarbon Accumulation Histories in the Kelasu-Yiqikelike Structural Belt of the Kuqa Foreland Basin, Acta Geologica Sinica (English Edition), vol. 84 no. 5, pp. 1195-1208.
Zhou, W, Zhao, G, Liu, F, Ye, B, Li, X, Huang, T, Zhou, Q, & Dong, G 2011, Characteristics of Hydrocarbon Accumulation in the Paleozoic Donghetang Formation in the Southwestern Tahe Area, Acta Geologica Sinica (English Edition), vol. 85 no. 4, pp. 899-910.
Order from us for quality, customized work in due time of your choice.