Automated Decline Curve and Production Analysis Using Automated Production Segmentation, Empirical Modeling, and Artificial Intelligence

Abstract

A computer-implemented method for automated decline curve and production analysis using automated production segmentation, empirical modeling, and artificial intelligence. The method includes segmenting historical production data based on a change in a central tendency of a selected segmentation parameter to generate segmented production data. The method also includes forecasting future production data from a last production segment to a terminal decline rate according to a fitted empirical model, a trained artificial intelligence model, or any combinations thereof. The method includes forecasting exponential production data to an economic limit. Further, the method includes calculating an estimated ultimate recovery by summing the historical production data, future production data, and the exponential production data.

Description

Claims

1. A computer-implemented method for automated decline curve and production analysis, the method comprising: segmenting, with one or more hardware processors, historical production data of a well based on a change in a central tendency of a segmentation parameter to generate segmented production data;forecasting, with the one or more hardware processors, future production data from a last production segment to a terminal decline rate according to a fitted empirical model, a trained artificial intelligence model, or any combinations thereof;forecasting, with the one or more hardware processors, exponential production data to an economic limit; andcalculating, with the one or more hardware processors, an estimated ultimate recovery of the well by summing the historical production data, future production data, and the exponential production data.

2. The computer implemented method of claim 1, comprising upscaling the historical production data prior to segmenting the historical production data, wherein the up-scaled historical production data minimizes noise.

3. The computer implemented method of claim 1, wherein the segmentation parameter is a driver of production changes over time.

4. The computer implemented method of claim 1, wherein segmenting comprises partitioning historical production data based on at least one flagged segmentation parameter change, wherein the central tendency outside of the flagged segmentation parameter change.

5. The computer implemented method of claim 1, wherein an empirical model is fit to the segmented production data by minimizing a difference between the segmented production data and predicted output of the empirical model to generate a fitted empirical model.

6. The computer implemented method of claim 1, wherein an artificial intelligence model is trained by dividing the segmented production data into a training dataset and a testing dataset, training the artificial intelligence model using the training dataset, and testing the trained artificial intelligence model using the testing dataset.

7. The computer implemented method of claim 1, comprising an exponential model to forecast exponential production data to the economic limit when the well reaches a terminal decline rate.

8. An apparatus comprising a non-transitory, computer readable, storage medium that stores instructions that, when executed by at least one processor, cause the at least one processor to perform operations comprising: segmenting historical production data of a well based on a change in a central tendency of a segmentation parameter to generate segmented production data;forecasting future production data from a last production segment to a terminal decline rate according to a fitted empirical model, a trained artificial intelligence model, or any combinations thereof;forecasting exponential production data to an economic limit; andcalculating an estimated ultimate recovery of the well by summing the historical production data, future production data, and the exponential production data.

9. The apparatus of claim 8, comprising upscaling the historical production data prior to segmenting the historical production data, wherein the up-scaled historical production data minimizes noise.

10. The apparatus of claim 8, wherein the segmentation parameter is a driver of production changes over time.

11. The apparatus of claim 8, wherein segmenting comprises partitioning historical production data based on at least one flagged segmentation parameter change, wherein the central tendency outside of the flagged segmentation parameter change.

12. The apparatus of claim 8, wherein an empirical model is fit to the segmented production data by minimizing a difference between the segmented production data and predicted output of the empirical model to generate a fitted empirical model.

13. The apparatus of claim 8, wherein an artificial intelligence model is trained by dividing the segmented production data into a training dataset and a testing dataset, training the artificial intelligence model using the training dataset, and testing the trained artificial intelligence model using the testing dataset.

14. The apparatus of claim 8, comprising an exponential model to forecast exponential production data to the economic limit when the well reaches a terminal decline rate.

15. A system, comprising: one or more memory modules;one or more hardware processors communicably coupled to the one or more memory modules, the one or more hardware processors configured to execute instructions stored on the one or more memory models to perform operations comprising: segmenting historical production data of a well based on a change in a central tendency of a segmentation parameter to generate segmented production data;forecasting future production data from a last production segment to a terminal decline rate according to a fitted empirical model, a trained artificial intelligence model, or any combinations thereof;forecasting exponential production data to an economic limit; andcalculating an estimated ultimate recovery of the well by summing the historical production data, future production data, and the exponential production data.

16. The system of claim 15, comprising upscaling the historical production data prior to segmenting the historical production data, wherein the up-scaled historical production data minimizes noise.

17. The system of claim 15, wherein the segmentation parameter is a driver of production changes over time.

18. The system of claim 15, wherein segmenting comprises partitioning historical production data based on at least one flagged segmentation parameter change, wherein the central tendency outside of the flagged segmentation parameter change.

19. The system of claim 15, wherein an empirical model is fit to the segmented production data by minimizing a difference between the segmented production data and predicted output of the empirical model to generate a fitted empirical model.

20. The system of claim 15, wherein an artificial intelligence model is trained by dividing the segmented production data into a training dataset and a testing dataset, training the artificial intelligence model using the training dataset, and testing the trained artificial intelligence model using the testing dataset.

21. The system of claim 15, comprising an exponential model to forecast exponential production data to the economic limit when the well reaches a terminal decline rate.