US-12618326-B2 - Devices and methods for oil field specialty chemical development and testing

US12618326B2US 12618326 B2US12618326 B2US 12618326B2US-12618326-B2

Abstract

Technologies for specialty chemical development and testing include devices and methods for receiving a test description. The test description is indicative of test parameters for a test of a chemical formulation, which may be an oil field specialty chemical. The devices and methods may include searching a database of historical test results based on similarity to the test parameters to generate multiple candidate chemical formulations. The devices and methods may cluster the candidate chemical formulations with an unsupervised machine learning algorithm to select a representative chemical formulation for each cluster. The devices and methods may include training a predictor based on test results using a supervised machine learning algorithm. Multiple virtual formulations may be generated and performances of each virtual formulation may be predicted with the predictor. Other embodiments are described and claimed.

Inventors

Ming-Zhao JIN
Peiqi QIAO
Dingzheng Yang
Song Gao

Assignees

CHAMPIONX USA INC.

Dates

Publication Date: 20260505
Application Date: 20230207
Priority Date: 20220211

Claims (20)

1 . A computing device for specialty chemical development testing, the computing device comprising: a tester interface to receive a test description indicative of a test parameter for a test of a chemical formulation, wherein the test parameter comprises an oil field process parameter; and a pre-test recommendation module to (i) search a database of historical test results based on similarity to the test parameter of the test description to generate a plurality of search results, and (ii) generate a plurality of candidate chemical formulations in response to a search of the database, wherein each of the plurality of candidate chemical formulations is associated with a search result of the plurality of search results.
2 . The computing device of claim 1 , wherein the chemical formulation comprises an oil field specialty chemical.
3 . The computing device of claim 2 , wherein the oil field specialty chemical comprises a demulsifier, a dispersant, a scale inhibitor, a corrosion inhibitor, or a defoamer.
4 . The computing device of claim 1 , wherein the oil field process parameter comprises a geometrical location, a treating temperature, a treating pressure, a reservoir type, a crude oil pump method parameter, or a crude oil characterization.
5 . The computing device of claim 1 , wherein to search the database comprises to perform a multidimensional distance search of the historical test results based on the test parameter.
6 . The computing device of claim 1 , further comprising a formulation cluster module to: cluster the plurality of candidate chemical formulations with an unsupervised machine learning algorithm to generate a plurality of formulation clusters; and select a representative chemical formulation for each of the plurality of formulation clusters.
7 . The computing device of claim 6 , wherein: the tester interface is further to receive a plurality of test results in response to selection of the representative chemical formulation, wherein each of the plurality of test results is indicative of a performance indicator for a corresponding representative chemical formulation; and the computing device further comprises a formulation optimizer module to train a predictor with the plurality of test results using a supervised machine learning algorithm.
8 . The computing device of claim 7 , wherein the performance indicator comprises turbidity, top oil total water content, or water recovery speed.
9 . The computing device of claim 7 , wherein the predictor comprises a regressor.
10 . The computing device of claim 7 , wherein the formulation optimizer module is further to: generate a plurality of virtual formulation candidates, wherein each of the plurality of virtual formulation candidates is indicative of a proportion of a chemical; and predict a plurality of predicted results with the predictor in response to training of the predictor, wherein each of the plurality of predicted results is indicative of the performance indicator for a corresponding virtual formulation candidate of the plurality of virtual formulation candidates.
11 . The computing device of claim 10 , wherein: the tester interface is further to receive a plurality of second test results in response to prediction of the plurality of predicted results, wherein each of the plurality of second test results is indicative of a performance indicator for a corresponding virtual formulation candidate of the plurality of virtual formulation candidates; and the formulation optimizer module is further to train the predictor with the plurality of second test results using the supervised machine learning algorithm.
12 . A method for specialty chemical development testing, the method comprising: receiving, by a computing device, a test description indicative of a test parameter for a test of a chemical formulation, wherein the test parameter comprises an oil field process parameter; searching, by the computing device, a database of historical test results based on similarity to the test parameter of the test description to generate a plurality of search results; and generating, by the computing device, a plurality of candidate chemical formulations in response to searching the database, wherein each of the plurality of candidate chemical formulations is associated with a search result of the plurality of search results.
13 . The method of claim 12 , wherein searching the database comprises performing a multidimensional distance search of the historical test results based on the test parameter.
14 . The method of claim 12 , further comprising: clustering, by the computing device, the plurality of candidate chemical formulations with an unsupervised machine learning algorithm to generate a plurality of formulation clusters; and selecting, by the computing device, a representative chemical formulation for each of the plurality of formulation clusters.
15 . The method of claim 14 , further comprising: receiving, by the computing device, a test result in response to selecting the representative chemical formulation, wherein the test result is indicative of a performance indicator for a corresponding representative chemical formulation; training, by the computing device, a predictor with the test result using a supervised machine learning algorithm; generating, by the computing device, a plurality of virtual formulation candidates, wherein each of the plurality of virtual formulation candidates is indicative of a proportion of a chemical; and predicting, by the computing device, a plurality of predicted results with the predictor in response to training the predictor, wherein each of the plurality of predicted results is indicative of the performance indicator for a corresponding virtual formulation candidate of the plurality of virtual formulation candidates.
16 . A non-transitory, computer-readable storage media comprising a plurality of instructions that in response to being executed cause a computing device to: receive a test description indicative of a test parameter for a test of a chemical formulation, wherein the test parameter comprises an oil field process parameter; search a database of historical test results based on similarity to the test parameter of the test description to generate a plurality of search results; and generate a plurality of candidate chemical formulations in response to searching the database, wherein each of the plurality of candidate chemical formulation is associated with a search result of the plurality of search results.
17 . The computer-readable storage media of claim 16 , wherein to search the database comprises to perform a multidimensional distance search of the historical test results based on the test parameter.
18 . The computer-readable storage media of claim 16 , further comprising a plurality of instructions that in response to being executed cause the computing device to: cluster the plurality of candidate chemical formulations with an unsupervised machine learning algorithm to generate a plurality of formulation clusters; and select a representative chemical formulation for each of the plurality of formulation clusters.
19 . The computer-readable storage media of claim 18 , further comprising a plurality of instructions that in response to being executed cause the computing device to: receive a plurality of test results in response to selecting the representative chemical formulation, wherein each of the plurality of test results is indicative of a performance indicator for a corresponding representative chemical formulation; and train a predictor with the plurality of test results using a supervised machine learning algorithm.
20 . The computer-readable storage media of claim 19 , further comprising a plurality of instructions that in response to being executed cause the computing device to: generate a plurality of virtual formulation candidates, wherein each of the plurality of virtual formulation candidates is indicative of a proportion of a chemical; and predict a plurality of predicted results with the predictor in response to training the predictor, wherein each of the plurality of predicted results is indicative of the performance indicator for a corresponding virtual formulation candidate of the plurality of virtual formulation candidates.

Description

BACKGROUND Several types of specialty chemicals, such as demulsifiers, corrosion inhibitors, scale inhibitors, and defoamers, are used during oil and/or gas production. Due to complicated formulation and application scenarios, selection and development of the specialty chemicals is typically an empirical process. SUMMARY According to one aspect, of the disclosure, a computing device for specialty chemical development testing includes a tester interface and a pre-test recommendation module. The tester interface is to receive a test description indicative of a test parameter for a test of a chemical formulation, wherein the test parameter comprises an oil field process parameter. The pre-test recommendation module is to search a database of historical test results based on similarity to the test parameter of the test description to generate a plurality of search results, and generate a plurality of candidate chemical formulations in response to a search of the database. Each of the plurality of candidate chemical formulations is associated with a search result of the plurality of search results. In an embodiment, the chemical formulation comprises an oil field specialty chemical. In an embodiment, the oil field specialty chemical comprises a demulsifier, a dispersant, a corrosion inhibitor, or a defoamer. In an embodiment, the oil field process parameter comprises a geometrical location, a treating temperature, a treating pressure, a reservoir type, a crude oil pump method parameter, or a crude oil characterization. In an embodiment, to search the database comprises to perform a multidimensional distance search of the historical test results based on the test parameter. In an embodiment, the computing device further includes a formulation cluster module to cluster the plurality of candidate chemical formulations with an unsupervised machine learning algorithm to generate a plurality of formulation clusters; and select a representative chemical formulation for each of the plurality of formulation clusters. In an embodiment, the unsupervised machine learning algorithm comprises a k-means clustering algorithm. In an embodiment, the tester interface is further to receive a plurality of test results in response to selection of the representative chemical formulation, wherein each of the plurality of test results is indicative of a performance indicator for a corresponding representative chemical formulation. In an embodiment, the performance indicator comprises turbidity, top oil total water content, or water recovery speed. In an embodiment, the computing device further includes a formulation optimizer module to train a predictor with the plurality of test results using a supervised machine learning algorithm. In an embodiment, the predictor comprises a regressor. In an embodiment, the predictor comprises a random forest classifier. In an embodiment, the formulation optimizer module is further to generate a plurality of virtual formulation candidates, wherein each of the plurality of virtual formulation candidates is indicative of a proportion of a chemical; and predict a plurality of predicted results with the predictor in response to training of the predictor, wherein each of the plurality of predicted results is indicative of the performance indicator for a corresponding virtual formulation candidate of the plurality of virtual formulation candidates. In an embodiment, the tester interface is further to receive a plurality of second test results in response to prediction of the plurality of predicted results, wherein each of the plurality of second test results is indicative of a performance indicator for a corresponding virtual formulation candidate of the plurality of virtual formulation candidates; and the formulation optimizer module is further to train the predictor with the plurality of second test results using the supervised machine learning algorithm. According to another aspect, a method for specialty chemical development testing includes receiving, by a computing device, a test description indicative of a test parameter for a test of a chemical formulation, wherein the test parameter comprises an oil field process parameter; searching, by the computing device, a database of historical test results based on similarity to the test parameter of the test description to generate a plurality of search results; and generating, by the computing device, a plurality of candidate chemical formulations in response to searching the database, wherein each of the plurality of candidate chemical formulations is associated with a search result of the plurality of search results. In an embodiment, the chemical formulation comprises an oil field specialty chemical. In an embodiment, the oil field specialty chemical comprises a demulsifier, a dispersant, a corrosion inhibitor, or a defoamer. In an embodiment, the oil field process parameter comprises a geometrical location, a treating temperature, a treating pressure,