Regional Pore Pressure Modeling Strategy at Tiger Shoal, GOM, USA

2008 ◽  
Author(s):  
Thomas W. Stafford ◽  
Rick Goode
Keyword(s):  
Pore Pressure ◽  
Modeling Strategy ◽  
Tiger Shoal

Successful Application of Customized Fluid Using Specialized Synthetic Polymer in High Pressured Wells to Mitigate Differential Stikcing Problems by Minimizing Pore Pressure Transmission

2014 ◽  
Author(s):  
Mohannad Sulaiman Al-Muhailan ◽  
Arun Rajagopalan ◽  
Al Aziz Khalid Al-Shayji ◽  
Prakash Balkrishna Jadhav ◽  
Faiz Ismail Khatib
Keyword(s):  
Pore Pressure ◽  
Synthetic Polymer ◽  
Pressure Transmission

Intervention Modeling

2017 ◽  
Author(s):  
Richard McCleary ◽  
David McDowall ◽  
Bradley J. Bartos
Keyword(s):  
Time Series ◽  
Autocorrelation Function ◽  
Moving Average ◽  
Intervention Model ◽  
Noise Component ◽  
Autoregressive Integrated Moving Average ◽  
Noise Function ◽  
Modeling Strategy ◽  
The Impact ◽  
True Structure

The general AutoRegressive Integrated Moving Average (ARIMA) model can be written as the sum of noise and exogenous components. If an exogenous impact is trivially small, the noise component can be identified with the conventional modeling strategy. If the impact is nontrivial or unknown, the sample AutoCorrelation Function (ACF) will be distorted in unknown ways. Although this problem can be solved most simply when the outcome of interest time series is long and well-behaved, these time series are unfortunately uncommon. The preferred alternative requires that the structure of the intervention is known, allowing the noise function to be identified from the residualized time series. Although few substantive theories specify the “true” structure of the intervention, most specify the dichotomous onset and duration of an impact. Chapter 5 describes this strategy for building an ARIMA intervention model and demonstrates its application to example interventions with abrupt and permanent, gradually accruing, gradually decaying, and complex impacts.

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