Well Control Simulation Model of Oil-Based Muds for HPHT Wells

Author:

An J..1,Lee K..2,Choe J..1

Affiliation:

1. Seoul National University

2. Korea Institute of Geoscience and Mineral Resources

Abstract

Abstract A kick is defined as an unscheduled flow of formation fluids into a wellbore. Kick control is crucial for safe drilling in high pressure high temperature (HPHT) wells. During drilling with oil-based mud (OBM) in these conditions, it is difficult to detect and control a kick because of high gas solubility and mud density change with pressure (P) and temperature (T). The main objective of this paper is to simulate behavior of kicks in HPHT conditions in offshore wells with OBM. To simulate kick behavior more accurately, it is necessary to consider mud density change with P and T. We select Standing-Katz correlation for base-oil density among three typical methods by comparing them with experimental data available. With the correlation, we update OBM density by use of Hoberock et al.'s compositional model in HPHT conditions. In this paper, by updating OBM density in Choe and Juvkam-Wold's modified two-phase well-control, kick behaviors are analyzed more realistically. We analyze several cases in 20,000 and 30,000 ft offshore wells with OBM. Under the condition of simulations, mud density decreases as well depth increases because temperature is more dominant than pressure. Pit volume does not always increase as the kick rises to surface because of combined effect of gas solubility, P, and T on kick volume. If we consider the density change of OBM, more surface choke pressure is needed for constant bottomhole pressure with deeper vertical depth and HPHT condition. By applying the proposed method, we can have realistic modeling of wellbore pressure profile and kick behaviors.

Publisher

SPE

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Mathematical modeling of oil and gas kick during drilling operations;11TH INTERNATIONAL CONFERENCE ON MATHEMATICAL MODELING IN PHYSICAL SCIENCES;2023

2. Artificial neural network model for predicting the density of oil-based muds in high-temperature, high-pressure wells;Journal of Petroleum Exploration and Production Technology;2019-11-22

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