A review of hydrocarbon allocation methods in the upstream oil and gas industry

Kanshio, Sunday

doi:10.1016/j.petrol.2019.106590

Cited by 13 publications

(7 citation statements)

References 6 publications

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“…Allocation methods are such digital solutions which are applied to flow measurement data of production systems, to minimize measurement errors and optimize the resulting sales allocation (Kanshio, 2020;Stockton and Spence, 2008). Proportional allocation methods, such as by-difference allocation (BDA) and pro-rata allocation (PRA), are widely used to mitigate measurement imbalances in oil and gas production systems (Amin, 2016;Bjørk et al, 2016;Lunde et al, 2002;Stewart and Skelton, 2004).…”

Section: Allocation Methodsmentioning

confidence: 99%

“…In order to reduce capital expenditure, oil and gas producers construct shared production or transport facilities and use joint flow metering for different wells (Johnsen and Dahl, 2018;Stewart and Skelton, 2004). Furthermore, fields can span over multiple concessions, and pipelines can be shared between stakeholders (Kanshio, 2020). Global production of natural gas is estimated at 4000 BCM (Alverà et al, 2018), while yearly growth rates are still around 1.5-2.0% (Bahadori, 2014).…”

Section: Introductionmentioning

confidence: 99%

“…The goal of the allocation process is to determine in the most fair manner the quantities of oil, gas, or other material produced and transported over a given period for each contributing producer (Amin, 2016;Kanshio, 2020). The resulting allocation should be auditable and defensible, and minimizes disputes between stakeholders in a production agreement (Pobitzer et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

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Data validation and reconciliation for error correction and gross error detection in multiphase allocation systems

Badings

Putten

2020

Journal of Petroleum Science and Engineering

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Section: Allocation Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Data validation and reconciliation for error correction and gross error detection in multiphase allocation systems

Badings

Putten

2020

Journal of Petroleum Science and Engineering

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“…The government is committed to ensuring that the hydrocarbon resources are developed in a manner that will be of maximum benefit to the nation whicle bearing in mind the imperative to maintain clear and competitive regulatory and royalty regimes in order to continue to attract new investments in the industry (Kanshio, 2020;Dagoumas et al, 2020). The contractual production sharing systems applies in Nigeria's deep-water operations.…”

Section: Deepwater Fiscal System Instruments In Nigeriamentioning

confidence: 99%

Nigeria Deep Offshore Inland Basin Production Sharing Contract Acts: Evaluating Contractor’s Take

Okoro

Echendu²,

Okoye³

et al. 2021

IJEEP

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Value-focused thinking is often designed to focus decisions on the essential activities that must occur prior to solving a decision problem. This approach was adopted by the Nigerian government in the fiscal legislation for Deep Offshore and Inland Basin Production Sharing Contract (DOIBPSC) Act enacted in 1999 and the subsequent amendment of 2020 version. One major item of interest in the amended Act is the introduction of royalty by price to enable the government to capture windfall in high oil price spike. This study evaluates the new fiscal regime to ascertain its attractiveness and impact on contractor take. Four features (royalty, cost recovery, tax oil, and profit oil) of the PSC contract terms were used to determine contractor and government takes from the transactions. This study adopted the full range of oil prices captured in the amended DOIBPSC Act in addition to the current market price of oil estimation.

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“…There is a large economic incentive for operators to maintain high production rates and avoid operational problems. Flow rates from individual wells support many important operational decisions, such as production optimization [2], reservoir management [3], and flow assurance [4].…”

Section: Introductionmentioning

confidence: 99%

Multi-task learning for virtual flow metering

Sandnes¹,

Grimstad²,

Kolbjørnsen³

2021

Preprint

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Virtual flow metering (VFM) is a cost-effective and non-intrusive technology for inferring multi-phase flow rates in petroleum assets. Inferences about flow rates are fundamental to decision support systems which operators extensively rely on. Data-driven VFM, where mechanistic models are replaced with machine learning models, has recently gained attention due to its promise of lower maintenance costs. While excellent performance in small sample studies have been reported in the literature, there is still considerable doubt towards the robustness of data-driven VFM. In this paper we propose a new multi-task learning (MTL) architecture for data-driven VFM. Our method differs from previous methods in that it enables learning across oil and gas wells. We study the method by modeling 55 wells from four petroleum assets. Our findings show that MTL improves robustness over single task methods, without sacrificing performance. MTL yields a 25-50% error reduction on average for the assets where single task architectures are struggling.

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A review of hydrocarbon allocation methods in the upstream oil and gas industry

Cited by 13 publications

References 6 publications

Data validation and reconciliation for error correction and gross error detection in multiphase allocation systems

Data validation and reconciliation for error correction and gross error detection in multiphase allocation systems

Nigeria Deep Offshore Inland Basin Production Sharing Contract Acts: Evaluating Contractor’s Take

Multi-task learning for virtual flow metering

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