Simulation of the effect of rock type on recovery plan of a mature carbonate oilfield in the Middle East – Part 1: Waterflooding recovery plan

Papi, Ali; Sharifi, Amin; Abdali, Mohammad Reza

doi:10.1080/10916466.2018.1550510

Cited by 1 publication

(1 citation statement)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Flow velocity [1] is an important monitoring parameter for oilfield development and it can evaluate the production of each oil reservoir [2]- [3]. Due to low velocity and high water cut, flow characteristics of oil-water two-phase flow [4]- [7] are very different from those of other conditions. The interpretation of log data [8]- [10] such as velocity depends on flow characteristics, so it is necessary to study the flow characteristics of oil-water two-phase flow at low flow velocity and high water cut state.…”

Section: Introductionmentioning

confidence: 99%

Oil Phase Velocity Measurement of Oil-Water Two-Phase Flow with Low Velocity and High Water Cut Using the Improved ORB and RANSAC Algorithm

Han

Wang

Yao

et al. 2020

Measurement Science Review

View full text Add to dashboard Cite

Velocity is an important parameter for fluid flow characteristics in profile logging. Particle tracking velocimetry (PTV) technology is often used to study the flow characteristics of oil wells with low flow velocity and high water cut, and the key to PTV technology is particle matching. The existing particle matching algorithms of PTV technology do not meet the matching demands of oil drops in the oil phase velocity measurement of oil-water two-phase flow with low velocity and high water cut. To raise the particle matching precision, we improved the particle matching algorithm from the oriented FAST and the rotated BRIEF (ORB) feature description and the random sample consensus (RANSAC) algorithm. The simulation and experiment were carried out. Simulation results show that the improved algorithm not only increases the number of matching points but also reduces the computation. The experiment shows that the improved algorithm in this paper not only reduces the computation of the feature description process, reaching half of the computation amount of the original algorithm, but also increases the number of matching results, thus improving the measurement accuracy of oil phase velocity. Compared with the SIFT algorithm and the ORB algorithm, the improved algorithm has the largest number of matching point pairs. And the variation coefficient of this algorithm is 0.039, which indicates that the algorithm is stable. The mean error of oil phase velocity measurement of the improved algorithm is 1.20 %, and the maximum error is 6.16 %, which is much lower than the maximum error of PTV, which is 25.89 %. The improved algorithm overcomes the high computation cost of the SIFT algorithm and achieves the precision of the SIFT algorithm. Therefore, this study contributes to the improvement of the measurement accuracy of oil phase velocity and provides reliable production logging data for oilfield.

show abstract