Field Application of an Interpretation Method of Downhole Temperature and Pressure Data for Detecting Water Entry in Horizontal/Highly Inclined Gas Wells

Achinivu, Ochi Ikoku (Texas A&M University) | Zhu, Ding (Texas A&M University) | Furui, Kenji (ConocoPhillips)

OnePetro 


Accurate and reliable downhole data acquisition has been made possible by advanced permanent monitoring systems such as downhole pressure and temperature gauges and fiber optic sensors. These downhole measurement instruments are increasingly incorporated as part of the intelligent completion in complex (highly slanted, horizontal, and multilateral) wells where they provide bottomhole temperature, pressure and sometimes volumetric flow rate along the wellbore. To fully realize the value of these
intelligent completions, there is a need for a systematic data analysis process to improve our understanding of reservoir and production conditions using the acquired data and to make decisions for well performance optimization.

We have successfully developed a model to predict well flowing pressure and temperature (i.e. the forward model), and applied inversion method to detect water and gas entry into wellbore using the synthetic data generated by the forward model (i.e. the inversion model) in the previous study. It is concluded that temperature profiles could provide sufficient information to identify fluid entries, especially in gas wells. However, both the mathematical complexity and advanced well structure lead to challenges in model validation and application. In this study, we applied the wellbore-reservoir flow coupled thermal simulation model to high-rate gas wells with field data. The main objectives are to evaluate applicability of the model to field problems, to study the sensitivity of parameters such as permeability and reservoir pressure on accuracy of interpretation, and to generate practical guidelines on how to initialize the inversion process. The model is applied to highly-slanted gas wells with water produced from a bottom aquifer. The interpretation result was compared against production logging data. The sensitivity of interpretation error to input reservoir properties are examined and the results showed that temperature and pressure anomalies caused by water production and flow rate changes can be detected theoretically and also practically. Judgments should be used based on the understanding of temperature and pressure behavior when initializing the forward model and this can increase efficiency
of model application. The study results and guidelines developed in this study will help us to design permanent monitoring systems and set realistic expectation for predictive capability of intelligent well systems.