Electrical modelling of an electrical submersible pump system three-phase power cable used in power line communication

During the process of oil collection, treating power cable for the supply of electrical submersible pump unit as the medium of the underground signal transmission to the ground, it has broad application prospects and economic value. But there is large distance between down-hole devices and ground equipment, and electrical submersible pump unit condition monitoring system measures many parameters, and there is a converter of harmonic interference, therefore, it restricted the application of the underground power line carrier communication technology severely. Designing an overall plan for signal extraction and transmission system in the paper. Based on the low-frequency power line carrier technology developing a practical communication protocol, to ensure the reliability of communication.

Download Full-text

Estimation of Bottom Hole Pressure in Electrical Submersible Pump Wells using Machine Learning Technique

10.2118/207122-ms ◽

2021 ◽

Author(s):

Sherif Sanusi ◽

Adenike Omisore ◽

Eyituoyo Blankson ◽

Chinedu Anyanwu ◽

Obehi Eremiokhale

Keyword(s):

Machine Learning ◽

Learning Algorithm ◽

Oil And Gas Industry ◽

Supervised Machine Learning ◽

Power Cable ◽

Submersible Pump ◽

Hole Pressure ◽

Bottom Hole Pressure ◽

Bottom Hole ◽

Electrical Submersible Pump

Abstract With the growing importance and application of Machine Learning in various complex operations in the Oil and Gas Industry, this study focuses on the implementation of data analytics for estimating and/or validating bottom-hole pressure (BHP) of Electrical Submersible Pump (ESP) wells. Depending on the placement of the ESP in the wellbore and fluid gravity of the well fluid, there can be little or no difference between BHP and Pump intake Pressure (PIP); hence these two parameters were used interchangeably. The study focuses majorly on validating PIP when there are concerns with downhole gauge readings. It also has application in estimating PIP when the gauge readings are not available, provided the relevant ESP parameters are obtainable. ESP wells generally have gauges that operate on "Comms-on-Power" principle i.e. downhole communication is via the power cable and loss of signal occurs when there is no good electrical integrity along the electrical path of the ESP system. For proper hydrocarbon accounting and statutory requirements, it is important to have downhole pressure readings on a continuous basis, however this cannot be guaranteed throughout the life cycle of the well. Therefore, an alternative method is essential and had to be sought. In this study, the Response Surface Modelling (RSM) was first used to generate a model relating the ESP parameters acquired real-time to the PIP values. The model was fine-tuned with a Supervised Machine Learning algorithm: Artificial Neural Network (ANN). The performance of the algorithms was then validated using the R-Square and Mean Square Error values. The result proves that Machine Learning can be used to estimate PIP in a well without recourse to incurring additional cost of deploying new downhole gauges for acquisition of well and reservoir data.

Download Full-text