Expert systems for well completion using bayesian decision networks including a fluids damage and temperature effects uncertainty node, a perforation considerations uncertainty node, a perforation analysis uncertainty node, and a perforation type decision node

• US 9,382,791 B2
• Filed: 07/24/2015
• Issued: 07/05/2016
• 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:
  US14/808,986
• Publication Number:
  US 20150330202A1
• Time in Patent Office: 347 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;

an underbalanced (UB) perforation utility uncertainty node configured to receive one or more UB perforation utilities from the one or more inputs;

a fluids damage and temperature effects uncertainty node dependent on the UB perforation utility uncertainty node and configured to receive one or more fluid damages, temperature effects, or a combination thereof from the one or more inputs;

a perforation considerations uncertainty node dependent on the fluids damage and temperature effects uncertainty node and configured to receive one or more perforation considerations from the one or more inputs;

a perforation analysis uncertainty node dependent on the perforation considerations uncertainty node and configured to receive one or more perforation analyses from the one or more inputs;

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

a completion type decision node configured to receive one or more completion types from the one or more inputs; and

a perforation consequences node dependent on the perforation analysis uncertainty node, the perforation type decision node, and the completion type decision node and configured to output one or more well completion recommendations based on one or more Bayesian probabilities calculated from the one or more perforation analyses, the one or more perforation types, and the one or more completion types.

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 zonal isolation types, the one or more reliability levels, the one or more cost levels, and the one or more productivity levels each associated with a respective plurality of probabilities.

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

a completion consequences node dependent on a zonal isolation types uncertainty node, a reliability level uncertainty node, a cost level uncertainty node, a productivity level uncertainty node, a junction classification decision node, and the completion type decision node, wherein the completion consequences node is configured to output the one or more well completion recommendations based on one or more Bayesian probabilities calculated from the one or more zonal isolation types input to the zonal isolation type uncertainty node, one or more reliability levels input to the reliability levels uncertainty node, one or more cost levels input to the one or more cost levels uncertainty node, one or more productivity levels input to the productivity levels uncertainty node, one or more junction classifications input to the junction classifications decision node, and the one or more completion types.

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;

an underbalanced (UB) perforation utility uncertainty;

a fluids damage and temperature effects uncertainty node dependent on the UB perforation utility uncertainty node;

a perforation considerations uncertainty node dependent on the fluids damage and temperature effects uncertainty node;

a perforation analysis uncertainty node dependent on the perforation considerations uncertainty node;

a perforation type decision node configured to receive one or more perforation types from the one or more inputs;

a completion type decision node; and

a consequences node dependent on the perforation analysis uncertainty node, the perforation type decision node, and the completion type 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 zonal isolation types uncertainty node, a reliability level uncertainty node, a cost level uncertainty node, a productivity level uncertainty, and the completion type decision node.