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The regression analysis is considered to be a very important tool for any manager, who wants to comprehend the relations between the different variables of a project and be able to predict the values different variables may have on each other. As a rule, there are the two types of possible regression models: a simple linear regression model where the two variables are possible as its maximum, and a multiple linear regression model where more than two possible variables are usually indentified.
In the article about the necessity to learn better the peculiarities of a drilling process and the trip time, the authors, Ardekani and Shadizadeh, make use of the two models for analysis, and one of them is a multiple linear regression model that aims at evaluating the relations that may take place between the properly defined drilling variables in order to predict and improve the outcomes of the drilling process. Though the multiple linear regression analysis used in the Ardekani and Shadizadeh’s article turns out to be not as effective as another type of model identified, it still helps to realise how different independent variables may be correlated and influence the dependent variable that is too crucial for the chosen process.
In the article, the multiple linear regression analysis consists of several steps. First of all, the process for the analysis should be identified. It is drilling venture. Second, the goals of drilling should be stated to comprehend why the use of a multiple linear regression model may be necessary. The main purpose is to minimise the costs at the expense of the trip time. Then, it is time to clear up the variables that should be used in a model. Three oil fields and their reports are used in the article in order to
- define the depth,
- open hole length,
- drill collars,
- top drive rigs,
- down hole motor,
- bit diameter size,
- mud weight
as the independent variable for the model and the trip time as the only crucial dependent variable. As soon as this step is taken, the correlation between the variables (dependent-independent and independent-independent) is made. However, some problems take place as one insignificant variable (bit diameter size) with the coefficient equal to or below zero is identified. At the end, it is concluded that the multiple linear regression model may be used in order to learn better the relations between different variables; still, the results achieved are appropriate and reliable for the chosen fields only. In other words, the use of the multiple linear regression model cannot be general. It should touch upon some specific details and their impact on the process considered.
In general, the multiple linear regression model is not complex in comparison to other models that have the same aims. Still, it has some disadvantages and creates certain challenges for the researchers. Unfortunately, the results of the article show that it is hard to give some common suggestions on how to minimise drilling costs with the help of a properly identified trip time, the article proves the multiple linear regression is a helpful approach to understanding how business, or social and behavioural sciences, or even some biological sciences may be improved or, at least, better understood.
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