Systems and methods for expert systems for underbalanced drilling operations using bayesian decision networks

Abstract

Systems and methods are provided for an underbalanced drilling (UBD) expert system that provides underbalanced drilling recommendations, such as best practices. The UBD expert system may include one or more Bayesian decision network (BDN) model that receive inputs and output recommendations based on Bayesian probability determinations. The BDN models may include: a general UBD BDN model, a flow UBD BDN model, a gaseated (i.e., aerated) UBD BDN model, a foam UBD BDN model, a gas (e.g., air or other gases) UBD BDN model, a mud cap UBD BDN model, an underbalanced liner drilling (UBLD) BDN model, an underbalanced coil tube (UBCT) BDN model, and a snubbing and stripping BDN model.

Description

PRIORITY CLAIM[0001]This application claims priority to U.S. Provisional Patent Application No. 61 / 722,027 filed on Nov. 2, 2012, entitled “Systems and Methods for Expert Systems for Underbalanced Drilling Operations Using Bayesian Decision Networks,” the disclosure of which is hereby incorporated by reference in its entirety.BACKGROUND OF THE INVENTION[0002]1. Field of the Invention[0003]This invention relates generally to the drilling and extraction of oil, natural gas, and other resources, and more particularly to evaluation and selection of underbalanced drilling systems.[0004]2. Description of the Related Art[0005]Oil, gas, and other natural resources are used for numerous energy and material purposes. The search for extraction of oil, natural gas, and other subterranean resources from the earth may cost significant amounts of time and money. Once a resource is located, drilling systems may be used to access the resources, such as by drilling into various geological formations ...

AI Technical Summary

Benefits of technology

[0018]Moreover, in some embodiments a system is provided having one or more processors and a non-transitory tangible computer-readable memory accessible by the one or more processors. The memory includes an underbalanced drilling expert system executable by the one or more processors and configured to provide one or more underbalanced drilling recommendations based on one or more inputs. The underbalanced drilling expert system includes an underbalanced liner drilling (UBLD) Bayesian decision network (BDN) model. The UBLD BDN model includes a first section having a UBLD plans uncertainty node configured to receive one or more UBLD plans from the one or more inputs, a UBLD plans recommendations decision node configured to receive one or more UBLD plans recommendations from the one or more inputs, and a first consequences node dependent on the UBLD planning uncertainty node and the UBLD planning recommendations decision node and configured to output the one or more underbalanced liner drilling recommendations based on one or more Bayesian probabilities calculated from the one or more UBLD plans and the one or more UBLD plans recommendations. The UBLD BDN model also includes a second section having a UBLD solvable problems uncertainty node configured to receive one or more UBLD solvable problems from the one or more inputs, a UBLD advantages decision node configured to receive one or more UBLD advantages from the one or more inputs, and a second consequences node dependent on the UBLD problems uncertainty node and the UBLD advantages decision node and configured to output the one or more underbalanced drilling recommendations based on one or more Bayesian probabilities calculated from the one or more UBLD solvable problems and the one or more UBLD advantages. Additionally, the UBLD BDN model includes a third section having a UBLD considerations uncertainty node configured to receive one or more UBLD considerations from the one or more inputs, a UBLD considerations recommendations decision node configured to receive one or more UBLD considerations recommendations from the one or more inputs, and a third consequences node dependent on the UBLD considerations uncertainty node and the UBLD recommendations decision node and configured to output the one or more underbalanced liner drilling recommendations based on one or more Bayesian probabilities calculated from the one or more UBLD considerations and the one or more UBLD considerations recommendations.

[0019]In some embodiments a computer-implemented method is provided for an underbalanced drilling expert system having an underbalanced drilling liner (UBLD) Bayesian decision network (BDN) model. The method includes receiving one or more inputs and providing the one or more inputs to one or more nodes of a first section of the UBLD BDN model. The one or more nodes include a UBLD plans uncertainty node configured to receive one or more UBLD plans and a UBLD plans recommendations decision node configured to receive one or more UBLD plans recommendations. The method also includes determining one or more underbalanced drilling recommendations at a consequences node of the first section of the UBLD BDN model by calculating one or more Bayesian probabilities based on the one or more inputs and providing the one or more underbalanced drilling recommendations to a user.

[0020]Moreover, in some embodiments a system is provided having one or more processors and a non-transitory tangible computer-readable memory accessible by the one or more processors. The memory includes an underbalanced drilling expert system executable by the one or more processors and configured to provide one or more underbalanced drilling recommendations based on one or more inputs. The underbalanced drilling expert system includes an underbalanced coil tube (UBCT) Bayesian decision network (BDN) model. The UBCT BDN model includes a first section having a UBCT preplanning uncertainty node configured to receive one or more UBCT preplans from the one or more inputs, a UBCT preplanning requirements decision node configured to receive one or more UBCT preplan requirements from the one or more inputs, a first consequences node dependent on the UBCT preplanning uncertainty node and the UBCT preplanning recommendations decision node and configured to output the one or more UBCT drilling requirements based on one or more Bayesian probabilities calculated from the one or more UBCT preplans and the one or more UBCT preplan requirements. The UBCT BDN model also includes a second section having a UBCT considerations uncertainty node configured to receive one or more UBCT considerations from the one or more inputs, a UBCT recommendations decision node configured to receive one or more UBCT recommendations from the one or more inputs, and a second consequences node dependent on the UBCT considerations uncertainty node and the UBCT recommendations decision node and configured to output the one or more underbalanced drilling recommendations based on one or more Bayesian probabilities calculated from the one or more UBCT considerations and the one or more UBCT recommendations.

[0021]In some embodiments

Problems solved by technology

The search for extraction of oil, natural gas, and other subterranean resources from the earth may cost significant amounts of time and money.

However, these techniques may be time

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