Systems and methods for expert systems for well completion using Bayesian decision models (BDNs), drilling fluids types, and well types

• US 9,140,112 B2
• Filed: 03/14/2013
• Issued: 09/22/2015
• Est. Priority Date: 11/02/2012
• Status: Expired due to Fees

• 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:
  US13/827,581
• Publication Number:
  US 20140124264A1
• Time in Patent Office: 922 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;

an expert data repository configured to store expert data; and

non-transitory tangible computer-readable memory in communication with the one or more processors, 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 drilling fluids uncertainty node configured to receive one or more drilling fluids from the one or more inputs, each of the one or more drilling fluids associated with a respective one or more drilling fluid probabilities algorithmically derived from the expert data,

a well types uncertainty node configured to receive one or more well types from the one or more inputs, each of the one or more well types associated with a respective one or more well type probabilities algorithmically derived from the expert data,

a packer selection node configured to receive one or more packer selections from the one or more inputs, each of the one or more packers associated with a respective one or more packer probabilities algorithmically derived from the expert data, a selection of packers available for input affected by three conditions, the three conditions consisting of;

a wellbore fluid selection,

a hydrocarbon selection, and

a completion fluid selection,

a treatment fluids decision node configured to receive one or more treatment fluids from the one or more inputs, each of the one or more treatment fluids associated with a respective one or more treatment fluid probabilities algorithmically derived from the expert data, a selection of treatment fluids available for input affected by two conditions, the two conditions consisting of;

a drilling fluid type, and

a well type, and

a completion fluid consequences node dependent on the drilling fluids uncertainty node, the well types uncertainty 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 drilling fluids, the one or more well types, and the one or more treatment fluids, each of the one or more well completion recommendations including a respective recommended completion fluid, the recommended completion fluid derived, using Bayesian probability determinations, from one or more of a drilling fluid probability and a well type probability.

2. The system of claim 1, comprising a user interface configured to display the well completion BDN model, receive user selections of the one or more input, and allow selection of one or more well completion recommendations to be implemented in a well completion operation performed on a well.

3. 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;

accessing expert data from an expert data repository;

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 drilling fluids uncertainty node configured to receive one or more drilling fluids from the one or more inputs, each of the one or more drilling fluids associated with a respective one or more drilling fluid probabilities algorithmically derived from the expert data;

a well types uncertainty node configured to receive one or more well types from the one or more inputs, each of the one or more well types associated with a respective one or more well type probabilities algorithmically derived from the expert data;

a packer selection node configured to receive one or more packer selections from the one or more inputs, each of the one or more packers associated with a respective one or more packer probabilities algorithmically derived from the expert data, a selection of packers available for input affected by three conditions, the three conditions consisting of;

a wellbore fluid selection,

a hydrocarbon selection, and

a completion fluid selection;

a treatment fluids decision node configured to receive one or more treatment fluids from the one or more inputs, each of the one or more treatment fluids associated with a respective one or more treatment fluid probabilities algorithmically derived from the expert data, a selection of treatment fluids available for input affected by two conditions, the two conditions consisting of;

a drilling fluid type, and

a well type; and

a consequences node dependent on the drilling fluids uncertainty node, the well types uncertainty 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, each of the one or more well completion recommendations including a respective recommended completion fluid, the recommended completion fluid derived, using Bayesian probability determinations, from one or more of a drilling fluid probability and a well type probability; and

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

4. The computer-implemented method of claim 3, 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, the user interface further configured to allow selection of one or more well completion recommendations to be implemented in a well completion operation performed on a well.