Petroleum Reservoirs and Oil Production Mechanisms

Baruah, Bhargav; Kalita, Puja; Pandey, Lalit M.; Tiwari, Pankaj

doi:10.1007/978-981-16-5465-7_1

Cited by 3 publications

(1 citation statement)

References 48 publications

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“…There are three methods of aggregation development: electrolytic coagulation, flocculation with hydrophobizated reagents, and flocculation with polymers. For ferromagnetic particles, there are methods of magnetical coagulation (Figure 2) [19][20][21][22]. The deficiency of the majority of the facilities used for coagulation based on prior art is polishing of the internal surfaces because of the settlement of fine particles in the dimensions of 0,3-5 micrometers.…”

Section: Introductionmentioning

confidence: 99%

Mathematical Modeling of Motion of the Solid Particles in the Filter-Coalescer Wastewater Treatment

Safarov

2023

GEOMATE

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The purpose of this article is to describe the method of calculation of the motion of particles in the filter-coalescer wastewater treatment based on the mathematical description of the motion of the cleaning fluid and the particles in the channel coagulator. It is found that fine particles in the coagulator move in a stream so that the process of consolidation and coalescence occurs at specific sites in the gap between the cones, with little flow velocity vector changes in direction and magnitude that the process provides an efficient aggregation of particles. The length of this section should be sufficient to ensure that the bulk of the particles reach a critical size. This allows the particles to migrate subsequent channel sections coagulator holding particles in the flow, and eliminating such deposition of particles on the internal surfaces of the device even when moving within the annular gap. The scheme of the electromagnetic filter of the coagulator, the two-dimensional scheme of the magnetic coagulator, the scheme of epure of speed vectors in the canal of the coagulator and the scheme of particle interaction in the canal of the coagulator are given too.

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Section: Introductionmentioning

confidence: 99%

Mathematical Modeling of Motion of the Solid Particles in the Filter-Coalescer Wastewater Treatment

Safarov

2023

GEOMATE

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Investigation of drive mechanisms in fractured oil reservoirs with tight matrix: a case study of an Iranian reservoir

Norouzi,

Rashidi,

Sedaee

2024

Geosystem Engineering

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Development of Machine Learning-Based Production Forecasting for Offshore Gas Fields Using a Dynamic Material Balance Equation

Hyoung,

Lee,

Han

2024

Energies

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Offshore oil and gas fields pose significant challenges due to their lower accessibility compared to onshore fields. To enhance operational efficiency in these deep-sea environments, it is essential to design optimal fluid production conditions that ensure equipment durability and flow safety. This study aims to develop a smart operational solution that integrates data from three offshore gas fields with a dynamic material balance equation (DMBE) method. By combining the material balance equation and inflow performance relation (IPR), we establish a reservoir flow analysis model linked to an AI-trained production pipe and subsea pipeline flow analysis model. We simulate time-dependent changes in reservoir production capacity using DMBE and IPR. Additionally, we utilize SLB’s PIPESIM software to create a vertical flow performance (VFP) table under various conditions. Machine learning techniques train this VFP table to analyze pipeline flow characteristics and parameter correlations, ultimately developing a model to predict bottomhole pressure (BHP) for specific production conditions. Our research employs three methods to select the deep learning model, ultimately opting for a multilayer perceptron (MLP) combined with regression. The trained model’s predictions show an average error rate of within 1.5% when compared with existing commercial simulators, demonstrating high accuracy. This research is expected to enable efficient production management and risk forecasting for each well, thus increasing revenue, minimizing operational costs, and contributing to stable plant operations and predictive maintenance of equipment.

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Petroleum Reservoirs and Oil Production Mechanisms

Cited by 3 publications

References 48 publications

Mathematical Modeling of Motion of the Solid Particles in the Filter-Coalescer Wastewater Treatment

Mathematical Modeling of Motion of the Solid Particles in the Filter-Coalescer Wastewater Treatment

Investigation of drive mechanisms in fractured oil reservoirs with tight matrix: a case study of an Iranian reservoir

Development of Machine Learning-Based Production Forecasting for Offshore Gas Fields Using a Dynamic Material Balance Equation

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