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Dcapy Tutorial - Introduction

This tutorial aims to show how to use Dcapy with most of its features. It follows a workflow built throught a Jupyter Notebook which can be replicated by the user.

Each section gradually depends on the previous ones in order to build well defined workflows according with the needs.

Sections

This tutorial is devided in the next sections in order to explain all the modules included:

  • Dca Module: It contains the step-by-step instructions to build basic forecast using Arps and Wor (so far) methodologies. Introduces the main concepts to declare outputs and how the classes relates with each other.

  • Filters Module: It contains the explination for the time series filters (Working on it!) used basically to fit external data to Arps and Wor models

  • Weiner Module: It explain how to get simple random walks simulations including the Geometric Brownonian Motion to be used, for example, to model the oil prices probabilistically.

  • Models Module: This section explains how to implement the scheduling and cashflow features to evaluate groups of forecast, scenarios and wells not only in production behavior but also their economics indexes.