Methods and systems for prediction of an optimal range for gas lift injection and optimal liquid production rate for oil wells

Abstract

Embodiments relate to acquiring well, reservoir and production data, synthesizing training and test data, and then constructing, training, and utilizing machine-learning models to: (i) predict whether or not gas lift should be applied to facilitate the production of subsurface fluids from an oil well, (ii) predict an optimal range of gas lift values to be used in the production of fluids from the oil well, and (iii) predict an optimal liquid production rate for the oil well when the gas lift value is within the predicted optimal range. Unlike traditional approaches, the disclosed embodiments do not require the use of well interventions, which eliminates production losses, delays, and costs. The disclosed embodiments also avoid the delays, biases, and non-optimized values associated with existing trial-and-error-based approaches to gas lift injection optimization. Disclosed embodiments enable the efficient and reliable determination of the optimal range of gas lift values and the optimal liquid production rate.

What is claimed is: 
     
         1 . A computing apparatus, comprising:
 at least one memory;   at least one processor configured to execute instructions stored in the memory to perform actions comprising:
 receiving, from one or more data sources, well data, reservoir data, and historical production data related to an oil well; 
 generating synthetic datasets from the well data, the reservoir data, and the historical production data, each synthetic dataset including a plurality of samples; 
 for each sample of the plurality of samples of each synthetic dataset, performing nodal analysis of the sample to generate a respective gas lift performance curve and to calculate corresponding target values, the corresponding target values including applicability of gas lift injection, a target minimum gas lift injection rate, a target maximum gas lift injection rate, and a target optimal liquid production rate for the sample; 
 generating engineered features from the well data, the reservoir data, and the historical production data; 
 selecting features from the well data, the reservoir data, the historical production data, and the engineered features; 
 constructing classification and regression models using the selected features as inputs to the models; 
 using a training portion of the plurality of samples of the synthetic datasets and the corresponding target values to train the classification and regression models to generate a trained classification model and a trained regression model, the trained classification model configured to predict applicability of gas lift injection to the oil well and the trained regression model configured to predict optimal minimum gas lift injection rates, optimal maximum gas lift injection rates, and optimal liquid production rates for the oil well. 
   
     
     
         2 . The computing apparatus of  claim 1 , wherein the at least one processor is further configured to execute instructions stored in the memory to perform actions comprising:
 using a testing portion of the plurality of samples of the synthetic datasets and the corresponding target values to evaluate the trained classification model and the trained regression model; and   selecting a best-performing trained classification model to predict applicability of gas lift injection to the oil well, and selecting a best-performing trained regression models to predict optimal minimum gas lift injection rates, optimal maximum gas lift injection rates, and optimal liquid production rates for the oil well.   
     
     
         3 . The computing apparatus of  claim 2 , wherein the at least one processor is further configured to execute the instructions stored in the memory to perform actions comprising:
 receiving current production data from one or more components of the oil well;   providing portions of the well data, reservoir data, and the current production data as inputs to features of the selected trained classification model and the selected trained regression models;   in response to the inputs, receiving, from the selected trained classification model, a binary output indicating whether or not gas lift injection is applicable for use in the oil well; and   in response to the inputs, receiving, from the selected trained regression models, a predicted minimum gas lift injection rate and a predicted maximum gas lift injection rate for the oil well, as well as a predicted optimal liquid production rate for the oil well when the predicted minimum gas lift injection rate is applied.   
     
     
         4 . The computing apparatus of  claim 3 , wherein the at least one processor is further configured to execute the instructions stored in the memory to perform actions comprising:
 providing control signals to a gas lift compressor and/or a gas lift control valve of the oil well to provide gas lift injection at the predicted minimum gas lift injection rate.   
     
     
         5 . The computing apparatus of  claim 2 , wherein constructing the classification and the regression models comprises:
 defining a logistic regression model as the classification model; and   defining a gradient boosting regression model and a random forest regression model as the regression models.   
     
     
         6 . The computing apparatus of  claim 1 , wherein each synthetic dataset includes at least 200 samples. 
     
     
         7 . The computing apparatus of  claim 1 , wherein:
 the well data is selected from the group consisting of well inclination, the inner diameter of the tubing or tubing identification, the inner diameter of the wellbore casing or casing identification, the depths at which the gas lift valves are installed (GLV depths), and the pressure decrease across the gas lift valves (dp Loss Actoss Valve);   the reservoir data is selected from the group consisting of reservoir depth, reservoir pressure, reservoir thickness, permeability, skin, and oil density; and   the historical production data is selected from the group consisting of wellhead pressure, gas lift injection pressure, gas lift injection rate, water cut, and gas-oil ratio (GOR).   
     
     
         8 . The computing apparatus of  claim 1 , wherein the at least one processor is further configured to execute the instructions stored in the memory to perform actions comprising:
 determining, based on the nodal analysis, a sensitivity of the liquid production rate to changes in the gas lift injection rate for each sample; and   assigning a sensitivity value to each sample based on the determined sensitivity;   wherein the respective gas lift performance curve is generated by plotting the sensitivity of the liquid production to changes in the gas lift injection rate.   
     
     
         9 . A method for enhancing oil production in an oil well, the method comprising:
 providing a controller in signal communication with a gas lift compressor and/or a gas lift control valve, the controller comprising at least one memory and at least one processor configured to execute instructions stored in the memory, the at least one memory storing a trained optimal gas lift injection range model, the trained optimal gas lift injection range model constructed using selected features as model inputs and trained using a training portion of a plurality of samples of synthetic data sets and corresponding target values, the selected features comprising raw features and engineered features determined based on well data, reservoir data, and historical production data related to the oil well, the synthetic data sets generated from the well data, reservoir data, and historical production data, the corresponding target values calculated based on nodal analysis of each sample of the plurality of samples of each synthetic dataset, the corresponding target values comprising a target minimum gas lift injection rate and a target maximum gas lift injection rate;   determining, using the trained optimal gas lift injection range model, a predicted optimal range of gas lift injection values for the oil well, the predicted optimal range extending from a predicted minimum gas lift injection rate to a predicted maximum gas lift injection rate; and   providing control signals to the gas lift compressor and/or the gas lift control valve so as to provide gas lift at the predicted minimum gas lift injection rate.   
     
     
         10 . The method of  claim 9 , wherein the at least one memory further stores a trained optimal liquid production model, the trained optimal liquid production model constructed using selected features as model inputs and trained using a training portion of a plurality of samples of synthetic data sets and corresponding target values, the selected features comprising raw features and engineered features determined based on well data, reservoir data, and historical production data related to the oil well, the synthetic data sets generated from the well data, reservoir data, and historical production data, the corresponding target values calculated based on nodal analysis of each sample of the plurality of samples of each synthetic dataset, the corresponding target values comprising a target optimal liquid production rate; the method further comprising:
 determining, using the trained optimal liquid production model, a predicted optimal liquid production rate when gas lift is provided to the oil well at the predicted minimum gas lift injection rate.   
     
     
         11 . The method of  claim 10 , wherein the at least one memory further stores a trained gas lift injection applicability classification model, the trained gas lift injection applicability classification model constructed using selected features as model inputs and trained using a training portion of a plurality of samples of synthetic data sets and corresponding target values, the selected features comprising raw features and engineered features determined based on well data, reservoir data, and historical production data related to the oil well, the synthetic data sets generated from the well data, reservoir data, and historical production data, the corresponding target values calculated based on nodal analysis of each sample of the plurality of samples of each synthetic dataset, the corresponding target values comprising applicability of gas lift injection; the method further comprising:
 determining, using the trained gas lift injection applicability classification model, whether gas lift injection is applicable for use in the oil well.   
     
     
         12 . The method of  claim 11 , further comprising:
 receiving, at the controller, well data, reservoir data, and current production data relating to the oil well; and   fine-tuning, at the controller, the trained optimal gas lift injection range model, the trained optimal liquid production model, and the trained gas lift injection applicability classification model based on an actual production rate of the well and the predicted optimal production rate as a result of providing gas lift at the predicted minimum gas lift injection rate.   
     
     
         13 . The method of  claim 12 , further comprising:
 providing at least a portion of the well data, the reservoir data, and the current production data as inputs to features of the trained gas lift injection applicability classification model; and   determining, using the trained gas lift injection applicability classification model, a binary output indicating whether or not gas lift injection continues to be applicable for use in the oil well.   
     
     
         14 . The method of  claim 9 , wherein:
 the well data is selected from the group consisting of well inclination, the inner diameter of the tubing or tubing identification, the inner diameter of the wellbore casing or casing identification, the depths at which the gas lift valves are installed (GLV depths), and the pressure decrease across the gas lift valves (dp Loss Actoss Valve);   the reservoir data is selected from the group consisting of reservoir depth, reservoir pressure, reservoir thickness, permeability, skin, and oil density; and   the historical production data is selected from the group consisting of wellhead pressure, gas lift injection pressure, gas lift injection rate, water cut, and gas-oil ratio (GOR).   
     
     
         15 . An oil production system comprising:
 a tubing disposed in a wellbore and fluidly coupling an interior of the wellbore to a wellhead;   a gas lift compressor operable to receive and compress a gas from a gas source to produce a compressed gas;   a gas lift control valve operable to receive the compressed gas produced by the gas lift compressor and direct the compressed gas into the wellbore on the outside of the tubing at a gas lift injection rate; and   a controller in signal communication with the gas lift compressor and/or the gas lift control valve and communicatively connected to one or more data sources, the controller configured to receive well data, reservoir data, and historical production data from the one or more data sources, the controller comprising at least one memory and at least one processor configured to execute instructions stored in the memory, the at least one memory storing a trained optimal gas lift injection range model, the trained optimal gas lift injection range model constructed using selected features as model inputs and trained using a training portion of a plurality of samples of synthetic data sets and corresponding target values, the selected features comprising raw features and engineered features determined based on well data, reservoir data, and historical production data related to the oil well, the synthetic data sets generated from the well data, reservoir data, and historical production data, the corresponding target values calculated based on nodal analysis of each sample of the plurality of samples of each synthetic dataset, the corresponding target values comprising a target minimum gas lift injection rate and a target maximum gas lift injection rate;   the controller further configured to determine, using the trained optimal gas lift injection range model, a predicted optimal range of gas lift injection values for the oil well, the predicted optimal range extending from a predicted minimum gas lift injection rate to a predicted maximum gas lift injection rate, and provide control signals to the gas lift compressor and/or the gas lift control valve to automatically modify the gas lift injection rate to be within the predicted optimal range of gas lift injection rate so as to facilitate an optimal liquid production rate of the produced fluids.   
     
     
         16 . The system of  claim 15 , wherein the one or more data sources is selected from the group consisting of the gas lift compressor and/or the gas lift control valve, a well and reservoir data source, a fluid analyzer, one or more pressure sensors, one or more temperature sensors, and one or more flow rate sensors. 
     
     
         17 . The system of  claim 16 , wherein:
 the well data is selected from the group consisting of well inclination, the inner diameter of the tubing or tubing identification, the inner diameter of the wellbore casing or casing identification, the depths at which the gas lift valves are installed (GLV depths), and the pressure decrease across the gas lift valves (dp Loss Actoss Valve);   the reservoir data is selected from the group consisting of reservoir depth, reservoir pressure, reservoir thickness, permeability, skin, and oil density; and   the historical production data is selected from the group consisting of wellhead pressure, gas lift injection pressure, gas lift injection rate, water cut, and gas-oil ratio (GOR).   
     
     
         18 . The system of  claim 17 , wherein the at least one memory further stores a trained optimal liquid production model, the trained optimal liquid production model constructed using selected features as model inputs and trained using a training portion of a plurality of samples of synthetic data sets and corresponding target values, the selected features comprising raw features and engineered features determined based on well data, reservoir data, and historical production data related to the oil well, the synthetic data sets generated from the well data, reservoir data, and historical production data, the corresponding target values calculated based on nodal analysis of each sample of the plurality of samples of each synthetic dataset, the corresponding target values comprising a target optimal liquid production rate;
 wherein the controller is further configured to determine, using the trained optimal liquid production model, a predicted optimal liquid production rate when gas lift is provided to the oil well at the predicted minimum gas lift injection rate.   
     
     
         19 . The system of  claim 18 , wherein the at least one memory further stores a trained gas lift injection applicability classification model, the trained gas lift injection applicability classification model constructed using selected features as model inputs and trained using a training portion of a plurality of samples of synthetic data sets and corresponding target values, the selected features comprising raw features and engineered features determined based on well data, reservoir data, and historical production data related to the oil well, the synthetic data sets generated from the well data, reservoir data, and historical production data, the corresponding target values calculated based on nodal analysis of each sample of the plurality of samples of each synthetic dataset, the corresponding target values comprising applicability of gas lift injection;
 wherein the controller is further configured to determine, using the trained gas lift injection applicability classification model, whether gas lift injection is applicable for use in the oil well.   
     
     
         20 . The system of  claim 19 , wherein the controller is further configured to:
 receive current production data from the one or more data sources;   provide at least a portion of the well data, the reservoir data, and the current production data as inputs to features of the trained gas lift injection applicability classification model; and   determine, using the trained gas lift injection applicability classification model, a binary output indicating whether or not gas lift injection continues to be applicable for use in the oil well.