Optimization of Operation Parameters for Helical Flow Cleanout with Supercritical CO2 in Horizontal Wells Using Back-Propagation Artificial Neural Network.

Xianzhi Song,Chi Peng,Gensheng Li,Haizhu Wang,Zhenguo He,Zhong-Ke Gao

doi:10.1371/journal.pone.0156358

Abstract

Sand production and blockage are common during the drilling and production of horizontal oil and gas wells as a result of formation breakdown. The use of high-pressure rotating jets and annular helical flow is an effective way to enhance horizontal wellbore cleanout. In this paper, we propose the idea of using supercritical CO2 (SC-CO2) as washing fluid in water-sensitive formation. SC-CO2 is manifested to be effective in preventing formation damage and enhancing production rate as drilling fluid, which justifies tis potential in wellbore cleanout. In order to investigate the effectiveness of SC-CO2 helical flow cleanout, we perform the numerical study on the annular flow field, which significantly affects sand cleanout efficiency, of SC-CO2 jets in horizontal wellbore. Based on the field data, the geometry model and mathematical models were built. Then a numerical simulation of the annular helical flow field by SC-CO2 jets was accomplished. The influences of several key parameters were investigated, and SC-CO2 jets were compared to conventional water jets. The results show that flow rate, ambient temperature, jet temperature, and nozzle assemblies play the most important roles on wellbore flow field. Once the difference between ambient temperatures and jet temperatures is kept constant, the wellbore velocity distributions will not change. With increasing lateral nozzle size or decreasing rear/forward nozzle size, suspending ability of SC-CO2 flow improves obviously. A back-propagation artificial neural network (BP-ANN) was successfully employed to match the operation parameters and SC-CO2 flow velocities. A comprehensive model was achieved to optimize the operation parameters according to two strategies: cost-saving strategy and local optimal strategy. This paper can help to understand the distinct characteristics of SC-CO2 flow. And it is the first time that the BP-ANN is introduced to analyze the flow field during wellbore cleanout in horizontal wells.

Highlights

Sand production is the result of formation breakdown
The results suggested that the community detection in two-phase flow complex network enabled to objectively distinguish intricate horizontal oil-water flow patterns, while the conventional method based on adaptive optimal kernel time-frequency representation (AOK TFR) was invalid
Vt implies the shear force applied to solid particles in the helical flow field, which helps suspend the particle

Summary

Introduction

Sand production is the result of formation breakdown. In horizontal wells, unexpected formation breakdown occurs when 1) the drilling fluid pressure exceeds the fracture pressure of formation; 2) the flowing bottom hole pressure is too low (or equivalently, the flow rate is too high) during production. The flow direction is perpendicular to the gravity, so the sands have a tendency to settle down, resulting in low cleanout efficiency or even worse troubles such as stuck-pipe [1]. Several improved cleanout techniques, such as wiper tripping and sand vacuuming [2], have been developed. Among these techniques, the helical flow approach, which applies partial high-pressure jets to generate spiral flow in annulus, has been verified to be efficient by simulations and field applications [3,4,5,6]. Song et al analyzed the mechanism and characteristics of helical flow in horizontal well cleanout with water-based fluids [2]

Methods

Results

Conclusion