Evaluation and Classification of Proved and Undeveloped Ooip by Reservoir Complexity Index (In Case of Salym Brown Oil Fields)

Naugolnov, M.V.; Gabitova, S.I.; Murtazin, Rustam; Klimov, V. Yu.

doi:10.24887/2587-7399-2020-1-44-48

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2021

2022

Publication Types

Select...

Other1

Article1

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Experience of the Development and Implementation of Cognitive Technologies for the Serbian Upstream Oil Industry

Naugolnov¹,

Ponjiger²,

Zalevskii³

2022

Day 3 Wed, October 05, 2022

View full text Add to dashboard Cite

The work is devoted to the experience of creation and implemention of digital solutions in the Petroleum Industry of Serbia (Serbian: NIS). The paper detaily examines digital projects which applied data science, machine learning/deep learning in seismic, petrophysics, geology, reservoir engineering and production technology. These projects aimed to solve current challenges of brown fields at a late stage of operation, as well as in working with sand-bearing well stock, control of scale and efficient development of fractured reservoirs.

show abstract

Experience of the Development and Implementation of Cognitive Technologies for the Serbian Upstream Oil Industry

Naugolnov¹,

Ponjiger²,

Zalevskii³

2022

Day 3 Wed, October 05, 2022

View full text Add to dashboard Cite

show abstract

Estimating recoverable oil reserves using integral displacement characteristics based on probabilistic methodology

Шумко¹,

Mamchistova²,

Kuzovlev³

2021

Izvestiâ vysših učebnyh zavedenij. Neftʹ i gaz

View full text Add to dashboard Cite

Estimation of recoverable oil reserves is an actual problem in field development. One way to estimate reserves is to use the characteristics of oil displacement by water. This method, in contrast to hydrodynamic modeling, doesn't take a long computational time and doesn't require information on the geological and filtration properties of the objects under consideration.The article discusses the use of integral displacement characteristics based on a probabilistic method for assessing potentially recoverable oil reserves. We describe an algorithm for estimating reserves by this method. In the course of the comparative analysis, the efficiency of the method was demonstrated depending on the watercut at the end of the approximation interval. As a result, with a watercut of less than 90 %, a better forecast was found than in the classical application of the characteristics of oil displacement by water.

show abstract

Evaluation and Classification of Proved and Undeveloped Ooip by Reservoir Complexity Index (In Case of Salym Brown Oil Fields)

Cited by 2 publications

References 0 publications

Experience of the Development and Implementation of Cognitive Technologies for the Serbian Upstream Oil Industry

Experience of the Development and Implementation of Cognitive Technologies for the Serbian Upstream Oil Industry

Estimating recoverable oil reserves using integral displacement characteristics based on probabilistic methodology

Contact Info

Product

Resources

About