Expert systems for well completion using Bayesian probabilities, and a packers consequence node dependent on wellbore fluids, hydrocarbon types, completion fluids, packers decision, and treatment fluids nodes

• US 9,371,726 B2
• Filed: 07/24/2015
• Issued: 06/21/2016
• Est. Priority Date: 11/02/2012
• Status: Active Grant

• Current Assignee: Saudi Arabian Oil Company (Government of Saudi Arabia), Texas A&M University System
• Original Assignee: Saudi Arabian Oil Company (Government of Saudi Arabia), Texas A&M University System
• Inventors: Al-Yami, Abdullah Saleh Hussain, Schubert, Jerome
• Primary Examiner(s): VINCENT, DAVID ROBERT
• Application Number:
  US14/808,849
• Publication Number:
  US 20150332159A1
• Time in Patent Office: 333 Days
• Field of Search: 706/12, 706/62, 706/45
• US Class Current: 1/1
• CPC Class Codes: E21B 44/00 Automatic control systems specially adapted for drilling operations, i.e. self-operating systems which function to carry out or modify a drilling operation without intervention of a human operator, e.g. computer-controlled drilling systems; Systems specially adapted for monitoring a plurality of drilling variables or conditions
• CPC Class Codes: G06N 5/045 Explanation of inference; Explainable artificial intelligence [XAI]; Interpretable artificial intelligence
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks

Abstract

Systems and methods are provided for expert systems for well completion using Bayesian decision networks to determine well completion recommendations. The well completion expert system includes a well completion Bayesian decision network (BDN) model that receives inputs and outputs recommendations based on Bayesian probability determinations. The well completion BDN model includes a treatment fluids section, a packer section, a junction classification section, a perforation section, a lateral completion section, and an open hole gravel packing section.

Claims

1. A system, comprising:

one or more processors;

a non-transitory tangible computer-readable memory, the memory comprising;

a well completion expert system executable by the one or more processors and configured to provide one or more well completion recommendations based on one or more inputs, the well completion expert system comprising a well completion Bayesian decision network (BDN) model, the well completion BDN model comprising;

a wellbore fluids uncertainty node configured to receive one or more wellbore fluids from the one or more inputs;

a hydrocarbon types uncertainty node configured to receive one or more hydrocarbon types from the one or more inputs;

a completion fluids uncertainty node configured to receive one or more completion fluids from the one or more inputs;

a packers decision node configured to receive one or more packers from the one or more inputs;

a treatment fluids decision node configured to receive one or more treatment fluids from the one or more inputs; and

a packers consequences node dependent on the wellbore fluids uncertainty node, the hydrocarbon types uncertainty node, the completion fluids uncertainty node, the packers decision node, and the treatment fluids decision node and configured to output the one or more well completion recommendations based on one or more Bayesian probabilities calculated from the one or more wellbore fluids, the one or more hydrocarbon types, the one or more completion fluids, the one or more packers, and the one or more treatment fluids.

2. The system of claim 1, comprising a user interface configured to display the well completion BDN model and receive user selections of the one or more input.

3. The system of claim 1, wherein the one or more wellbore fluids, the one or more hydrocarbon types, and the one or more completion fluids are each associated with a respective plurality of probabilities.

4. The system of claim 1, wherein the well completion BDN model comprises:

a completion fluid consequences node dependent on a drilling fluids uncertainty node, a well types uncertainty node, and the treatment fluids decision node, wherein the completion fluid consequences node is configured to output the one or more well completion recommendations based on one or more Bayesian probabilities calculated from one or more drilling fluids input to the drilling fluids uncertainty node, one or more well types input to the well types uncertainty node, and the one or more treatment fluids.

5. A computer-implemented method for a well completion expert system having a well completion Bayesian decision network (BDN) model, the method comprising:

receiving, at one or more processors, one or more inputs;

providing, by one or more processors, the one or more inputs to one or more nodes of the well completion BDN model, the one or more nodes comprising;

a wellbore fluids uncertainty node;

a hydrocarbon types uncertainty node;

a completion fluids uncertainty node;

a packers decision node;

a treatment fluids decision node; and

a consequences node dependent on the wellbore fluids uncertainty node, the hydrocarbon types uncertainty node, the completion fluids uncertainty node, the packers decision node, and the treatment fluids decision node;

determining, by one or more processors, one or more well completion recommendations at the consequences node of the well completion BDN model, the determination comprising a calculation of one or more Bayesian probabilities based on the one or more inputs; and

providing, by one or more processors, the one or more well completion recommendations to a user.

6. The computer-implemented method of claim 5, wherein providing, by one or more processors, the one or more well completion recommendations to a user comprises displaying the one or more well completion recommendations in a user interface element of a user interface configured to display the well completion BDN model.

7. The computer-implemented method of claim 5, comprising determining the one or more well completion recommendations at a second consequences node of the well completion BDN model, the determination comprising a calculation of one or more Bayesian probabilities based on the one or more inputs, wherein the one or inputs are provided to a drilling fluids uncertainty node, a well types uncertainty node, and the treatment fluids decision node.