Method of controlling development of an oil or gas reservoir

• US 6,002,985 A
• Filed: 05/06/1997
• Issued: 12/14/1999
• Est. Priority Date: 05/06/1997
• Status: Expired due to Term

• Current Assignee: Halliburton Energy Services Incorporated (Halliburton Company)
• Original Assignee: Halliburton Energy Services Incorporated (Halliburton Company)
• Inventors: Stephenson, Stanley V.
• Primary Examiner(s): McElheny, Jr., Donald E.
• Application Number:
  US08/851,919
• Time in Patent Office: 952 Days
• Field of Search: 702/9, 702/12, 702/13, 702/11, 702/6, 702/7, 706/929, 706/928
• US Class Current: 702/13
• CPC Class Codes: E21B 2200/22 Fuzzy logic, artificial intelligence, neural networks or the like
• CPC Class Codes: E21B 43/00 Methods or apparatus for obtaining oil, gas, water, soluble or meltable materials or a slurry of minerals from wells applicable only to water E03B
• 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: E21B 49/00 Testing the nature of borehole walls; Formation testing; Methods or apparatus for obtaining samples of soil or well fluids, specially adapted to earth drilling or wells
• CPC Class Codes: E21B 49/003 by analysing drilling variables or conditions E21B49/005 takes precedence; systems specially adapted for monitoring a plurality of drilling variables or conditions E21B44/00
• CPC Class Codes: Y10S 706/929 Geological, e.g. seismology

Abstract

A method controlling development of an oil or gas reservoir uses a neural network and genetic algorithm program to define a neural network topology and the optimal inputs for that topology. The topology is defined from identified and selected (1) parameters associated with the formation or formations in which actual wells are drilled in the reservoir and (2) parameters associated with the drilling, completion and stimulation of those wells and (3) parameters associated with the oil or gas production from the wells. Subsequent drilling, completion and stimulation of the reservoir is determined and applied based on hypothetical alternatives input to the topology and resulting outputs.

Claims

1. A method of controlling development of an oil or gas reservoir, comprising steps of:

(a) selecting an oil or gas reservoir, wherein the reservoir has a plurality of wells drilled therein from which oil or gas has been produced;

(b) identifying well drilling parameters associated with drilling of the plurality of wells;

(c) identifying well completion parameters associated with completing the plurality of wells;

(d) identifying well stimulation parameters associated with stimulating the plurality of wells;

(e) identifying formation parameters associated with the locations in the reservoir where the plurality of wells are drilled;

(f) identifying production parameters associated with the production of the oil or gas from the plurality of wells;

(g) selecting at least one drilling parameter, at least one completion parameter, at least one stimulation parameter, at least one formation parameter, and at least one production parameter from among the identified well drilling parameters, well completion parameters, well stimulation parameters, formation parameters, and production parameters;

(h) converting the selected parameters to encoded digital signals for a computer;

(i) defining in the computer a neural network topology representing a relationship between the selected drilling, completion, stimulation and formation parameters and the at least one selected production parameter in response to the encoded digital signals, including manipulating the encoded digital signals in the computer using genetic algorithms to define the neural network topology;

(j) entering into the computer as inputs to the defined neural network topology a first group of additional encoded digital signals representing proposed drilling, completion, stimulation and formation parameters of the same type as the selected drilling, completion, stimulation, and formation parameters, and generating an output from the defined neural network topology in response;

(k) repeating step (j) using at least a second group of additional encoded digital signals representing other proposed drilling, completion, stimulation and formation parameters; and

(l) controlling further development of the oil or gas reservoir in response to at least one of the generated outputs, including at least one step selected from the group consisting of (1) drilling at least one new well in the reservoir in response to the generated output and (2) treating at least one well in the reservoir in response to the generated output.

2. A method as defined in claim 1, wherein the step of drilling at least one new well in the reservoir includes selecting a location to drill the well in the reservoir in response to the generated output.

3. A method as defined in claim 1, wherein the step of treating at least one well includes forming a stimulation fluid and pumping the stimulation fluid into the well in response to the generated output.

4. A method as defined in claim 1, wherein step (l) further includes computing a cost for implementing the proposed drilling, well stimulation and formation parameters represented by the respective encoded digital signals of each group in steps (j) and (k);

computing a revenue for each of the generated outputs; and

selecting the generated output having the highest computed revenue to corresponding computed cost ratio as the generated output in response to which the further development of the reservoir is controlled.

5. A computer-implemented method of controlling development of an oil or gas reservoir by enabling an individual to observe through the operation of the computer a simulated production of oil or gas from the reservoir before an actual well is drilled in the reservoir to try to obtain therefrom actual production corresponding to the simulated production, said method comprising:

(a) selecting an oil or gas reservoir having a known configuration of equipment disposed therein defining a plurality of actual wells drilled in the reservoir and further having a plurality of known well implementation parameters and well production parameters for each of the actual wells;

(b) simulating each of the actual wells in the computer, including translating selected ones of the known parameters of the actual wells into encoded electrical signals for the computer and storing the encoded electrical signals in memory of the computer such that the encoded electrical signals representing the selected well implementation parameters for a respective actual well are associated with the encoded electrical signals representing the selected production parameters for the same respective well;

(c) determining with the computer a correlation for the reservoir between the types of the selected well implementation parameters and the types of production parameters in response to the plurality of simulated wells, including creating in the computer a neural network topology defining the correlation using predetermined genetic algorithms and the stored encoded electrical signals;

(d) indicating to the computer a proposed well for the reservoir, including translating well implementation parameters for the proposed well into encoded electrical signals and storing the encoded electrical signals in the computer;

(e) simulating with the computer a production from the proposed well, including generating an output representing the production in response to the encoded electrical signals of step (d) and the correlation of step (c) such that the generated output is correlated to the encoded electrical signals of step (d) by the correlation of step (c); and

(f) displaying for observation by an individual a representation of the simulated production.

6. A method as defined in claim 5, further comprising drilling an actual well in the reservoir based on the well implementation parameters of step (d), including selecting a location to drill the well in the reservoir in response to the displayed representation of the simulated production.

7. A method as defined in claim 6, further comprising treating the drilled well, including forming a stimulation fluid and pumping the stimulation fluid into the well in response to the well implementation parameters of step (d).

8. A method as defined in claim 5, further comprising:

repeating steps (d), (e) and (f) for a plurality of simulated proposed wells;

computing a cost for implementing the proposed well implementation parameters for each of the plurality of simulated proposed wells, and computing a revenue for each of the simulated productions for the proposed wells; and

drilling an actual well in the reservoir corresponding to the simulated proposed well having the highest ratio of computed revenue to corresponding computed cost.

9. A method of generating a model of an oil or gas reservoir in a digital computer for use in analyzing the reservoir, comprising:

providing the computer with a data base for a plurality of wells actually drilled in the reservoir, including parameters of physical attributes of the wells;

providing the computer with a neural network and genetic algorithm application program to define a neural network topology within the computer in response to the parameters in the data base; and

initiating the computer such that the neural network and genetic algorithms within the application program automatically define the neural network topology and the input data used to optimally form the topology in response to the data base of the parameters of physical attributes of the wells.

10. A method as defined in claim 9, further comprising:

determining a hypothetical set of parameters of physical attributes corresponding to at least some of the physical attribute parameters of the data base;

providing the computer with the determined hypothetical set of parameters;

calculating in the computer, using the defined neural network topology, a production parameter correlated to the hypothetical set of parameters; and

operating a display device in response to the calculated production parameter so that an individual viewing the display device tracks possible production from a well to which the hypothetical set of parameters is applied prior to any actual corresponding production occurring.

11. A method as defined in claim 10, further comprising drilling an actual well in the reservoir in response to the display of possible production.

12. A method as defined in claim 11, further comprising:

determining additional data and providing the additional data to the data base of the computer, including measuring and recording actual parameters of physical attributes of the actual well drilled in the reservoir; and

initiating the computer such that the neural network and genetic algorithm application program automatically operates within the computer to redefine the neural network topology in response to the data base of parameters of physical attributes of the wells, which data base includes the additional data.