EP-4739885-A1 - SYSTEM FOR RECOMMENDING A RESERVOIR MONITORING PLAN AND FLOW TESTING PLAN OF WELLS

Abstract

The present disclosure relates to computer-implemented method (300) for training a model for predicting a flow test of a well as well as a corresponding model for predicting a flow test of a well and a method (100) for determining a flow test plan. In addition, a computer-implemented method (400) for training a model for predicting saturation and pressure within a reservoir is disclosed as well as a corresponding model for predicting saturation and pressure within a reservoir and a method (200) for determining a reservoir monitoring plan. Finally, a corresponding computer program and data- processing system (500) is disclosed.

Inventors

• SAPUTELLI, LUIGI
• ABDOU, Medhat
• QUIJADA, Daniel
• MATA, CARLOS
• BADMAEV, Dorzhi
• SUBHASH KOLEKAR, Prakash
• SHAIK, Adul Raouf
• KAHAR, Zulkarnain

Assignees

• Abu Dhabi National Oil Company

Dates

Publication Date

20260513

Application Date

20230706

Claims (20)

1. 1. A computer-implemented method (300) for training a model for predicting a flow test of a well, wherein the method comprises the steps of: obtaining (310) a data sample comprising: a first set of parameters associated with at least a first flow test of the well; and a reference parameter associated with a second flow test of the well; providing (320) the first set of parameters associated with at least the first flow test of the well as input to the model; receiving (330) a predicted parameter associated with the second flow test of the well as output from the model; determining (340) an error between the predicted parameter and the reference parameter; and adjusting (350) one or more training parameters of the model based at least on the error between the predicted parameter and the reference parameter.
2. 2. The method of the preceding claim, wherein the first flow test of the well is associated with a first time stamp; wherein the second flow test of the well is associated with a second time stamp; and wherein the first time stamp is smaller than the second time stamp.
3. 3. The method of any one of the preceding claims, wherein each parameter of the first set of parameters associated with the first flow test is a measurement value resulting from the first flow test of the well.
4. 4. The method of any one of the preceding claims, wherein the reference parameter associated with the second flow test of the well is a measurement value resulting from the second flow test of the well; and wherein the predicted parameter is an estimation of the measurement value.
5. 5. The method of any one of the preceding claims, wherein the first set of parameters comprises at least one of: a choke opening of the well, a separator pressure, PSEP, of the well, a well-head pressure, WHP, of the well, a well-head temperature, WHT, of the well, a water-cut, WCT, of the well, a gas-oil ratio, GOR, of the well, an amount of oil produced, OIL, of the well, an amount of gaslift rate injected (GLR), a rotating speed of a pump (RPM) or any combination thereof.
6. 6. The method of any one of the preceding claims, wherein the reference parameter and the predicted parameter are one of: GOR, WCT, WHP, WHT or OIL.
7. 7. A model for predicting a flow test of a well, wherein the model is trained to: receive as input a time stamp for predicting a flow test of the well; and output a predicted parameter associated with the flow test of the well at the time stamp.
8. 8. The model of the preceding claim, wherein the model is trained according to anyone of the preceding claims 1 to 6.
9. 9. The model of any one of the preceding claims, wherein the model is an autoregressive integrated moving average with exogenous inputs, ARIMAX, model.
10. 10. The model of the preceding claim, wherein the training parameters of the model comprise one or more covariates of the ARIMAX model.
11. 11. The model of any one of the preceding claims, wherein the model comprises a plurality of sub models, wherein each sub model of the plurality of sub models is trained according to the method of any one of the preceding claims; and wherein each sub model of the plurality of sub models is trained to output a different predicted parameter.
12. 12. A computer-implemented method (too) for determining a flow test plan, the method comprising: determining (no) a first flow test frequency of a plurality of flow test frequencies for a well; providing (120) to a model a first time stamp as input to predict a first flow test at the first time stamp; receiving (130) from the model a first predicted parameter associated with the first flow test at the first time stamp; and determining (140) a second flow test frequency of the plurality of flow test frequencies for the well based on the first predicted parameter.
13. 13. The method of the preceding claim, wherein the model is one according to any one of the preceding claims 7-11.
14. 14. The method of any one of the preceding claims 12-13, wherein determining the first flow test frequency for the well comprises: assigning a test criticality level of a plurality of test criticality levels to the well; and determining the first flow test frequency based on the test criticality level.
15. 15. The method of the preceding claim, wherein the plurality of test criticality levels comprises: a low test criticality level, a medium test criticality level and a high test criticality level; and wherein the plurality of flow test frequencies comprises: a low testing frequency, preferably one every 60 days; a medium testing frequency, preferably one every 30 days; and a high testing frequency, preferably one every 15 days.
16. 16. The method of the preceding claim, wherein assigning the test criticality level to the well comprises: determining that a set of parameters associated with the first flow test fulfills a first predefined set of rules and assigning a low test criticality level as the test criticality level; or determining that the set of parameters associated with the first flow test fulfills a second predefined set of rules and assigning a medium test criticality level as the test criticality level; or determining that the set of parameters associated with the first flow test fulfills a third predefined set of rules and assigning a high test criticality level as the test criticality level.
17. 17. The method of the preceding claims 15-16, wherein determining the first flow test frequency based on the test criticality level comprises: determining that the first flow test frequency is a low testing frequency if the assigned test criticality level is low; or determining that the first flow test frequency is a medium testing frequency if the assigned test criticality level is medium; or determining that the first flow test frequency is a high testing frequency if the assigned test criticality level is high.
18. 18. The method of any one of the preceding claims 12-17, wherein determining the second flow test frequency comprises: determining a first error between the first predicted parameter associated with the first flow test at the first time stamp and a first reference parameter.
19. 19. The method of any one of the preceding claim, wherein the method further comprises: providing to the model at least a second time stamp as input to predict a second flow test at the second time stamp; wherein the second time stamp is smaller than the first time stamp; receiving from the model a second predicted parameter associated with the second flow test at the second time stamp; and wherein determining the second flow test frequency further comprises: determining a second error between the second predicted parameter associated with the second flow test at the second time stamp and a second reference parameter.
20. 20. The method of any one of the preceding claims 18-19, wherein determining the second flow test frequency further comprises: determining that the first error, the second error and/or a combination of the first error and the second error is above or equal to a predefined error threshold and increase the flow test frequency; or determining that the first error, the second error and/or a combination of the first error and the second error is below the predefined error threshold and decrease the flow test frequency.

Description

SYSTEM FOR RECOMMENDING A RESERVOIR MONITORING PLAN AND FLOW TESTING PLAN OF WELLS Field of the invention The present invention relates to a computer-implemented method for training a model for predicting a flow test of a well and a corresponding model for predicting a flow test. In addition, a computer-implemented method for determining a flow test plan is disclosed. Additionally, a computer-implemented method for training a model for predicting saturation and pressure with a reservoir and a corresponding model for predicting saturation and pressure within a reservoir is disclosed as well as corresponding computer-implemented method for determining a reservoir monitoring plan. Finally, the present invention relates to a corresponding computer program and data-processing system. Background Efficient reservoir management includes continuous optimization of field development opportunities (e.g., with respect to estimated oil remaining (EOR) or Infill), identifying or avoiding abnormal events (e.g., inactive strings) while at the same time meeting certain reservoir management objectives (e.g., workovers or rate changes). The corresponding decision making thus requires a solid data basis for assessing the reservoir state. However, acquiring reservoir data (e.g., surface and wellbore data for multiple wells of the reservoir) can become quite challenging in mature assets comprising a large number of wells, because such data acquisition implies high operating effort (e.g., with respect to required measurement equipment, measurement specialists etc.). These factors limit the data acquisition, which is why a tradeoff between data maturity and available resources is to be found. Against this background, there is a need for methods and system for improving reservoir monitoring and flow testing of wells. Summary The above-mentioned problem is at least partly solved by the aspects presented in the accompanying independent and dependent claims. Combinations of features from the dependent claims may be combined with features of the independent claims as appropriate and not merely as explicitly set out in the claims. An aspect of the present disclosure relates to a computer-implemented method for training a model for predicting a flow test of a well. The method may comprise the step of obtaining a data sample. The data sample may comprise a first set of parameters associated with at least a first flow test of the well and/or a reference parameter associated with a second flow test of the well. The method may comprise the step of providing the first set of parameters associated with at least the first flow test of the well as input to the model. The method may comprise the step of receiving a predicted parameter associated with the second flow test of the well as output from the model. The method may comprise the step of determining an error between the predicted parameter and the reference parameter. The method may comprise the step of adjusting one or more training parameters of the model at least based on the error between the predicted parameter and the reference parameter. This way the model is able to learn the relationship between conducted flow tests with respect to reference parameters. Accordingly, when using the model for prediction, the model is able to simulate a flow test and to predict the corresponding parameter. In a further aspect, the first flow test of the well may be associated with a first time stamp. The second flow test of the well may be associated with a second time stamp. The first time stamp may be smaller than the second time stamp. This way the model is able to learn temporal aspects between conducted flow tests, e.g., between the first flow test and the second (subsequent) flow test This way the model is able to simulate a flow test for a given time stamp in the future and to predict the corresponding parameter. In a further aspect, each parameter of the first set of parameters associated with the first flow test may be a measurement value resulting from the first flow test of the well. In a further aspect, the reference parameter associated with the second flow test of the well may be a measurement value resulting from the second flow test of the well. The predicted parameter maybe an estimation of the measurement value. Using real measurement parameters of a conducted flow tests as training data instead of for example synthetic data provides a higher data quality. Accordingly, the model is better able to learn and thus represent the real behavior of the well. In a further aspect, the first set of parameters may comprise at least one of: a choke opening of the well (CHK), a separator pressure of the well (PSEP), a well-head pressure (WHP) of the well, a well-head temperature (WHT) of the well, a water indicative quantity (e.g., one or more of a water-cut (WCT) of the well, a water oil ratio or a water rate), a gas indicative quantity (e.g., one or more of a gas-oil ratio (GOR) of the well