Inference Models for Well Production with Limited Training Data

Abstract

Example embodiments involve obtaining training data comprising a first set of features relating to drilling wells, a second set of features relating to geology at the well locations, a third set of features relating to spacings between wells, and a fourth set of features relating to production output of the wells; training a first model to predict the third set of features given the first and second set of features; determining a spacing error based on differences between the third set of features in the training data and as predicted; training a second model to predict the fourth set of features given the first and second set of features; determining production error based on differences between the fourth set of features in the training data and as predicted; and training a third model to predict the production error given the spacing error and the first and second set of features.

What is claimed is: 
     
         1 . A computer-implemented method comprising:
 obtaining training data comprising a first set of features relating to drilling of a set of wells, a second set of features relating to respective geological properties of locations at which the wells were drilled, a third set of features relating to spacings between the wells, and a fourth set of features relating to production output of the wells;   training a first machine-learning model to predict the third set of features given the first set of features and the second set of features;   determining a spacing error based on a difference between the third set of features appearing in the training data and as predicted;   training a second machine-learning model to predict the fourth set of features given the first set of features and the second set of features;   determining a production error based on a difference between the fourth set of features appearing in the training data and as predicted; and   training a third machine-learning model to predict the production error given the spacing error, the first set of features, and the second set of features, wherein the third set of features is not considered by the third machine-learning model, and wherein the production error is usable to estimate a further production output of wells with spacings not represented in the third set of features.   
     
     
         2 . The computer-implemented method of  claim 1 , wherein determining the spacing error comprises determining counterfactual examples for the third set of features, and wherein determining the production error is based on the counterfactual examples and true values of the third set of features, the computer-implemented method further comprising:
 applying the production error to the fourth set of features to generate counterfactual production values.   
     
     
         3 . The computer-implemented method of  claim 2 , further comprising:
 training a fourth machine-learning model to predict the counterfactual production values based on the first set of features and the second set of features, wherein the third set of features is not considered by the fourth machine-learning model.   
     
     
         4 . The computer-implemented method of  claim 2 , wherein the counterfactual examples for the third set of features are based on a measure of central tendency as applied to the spacings between wells. 
     
     
         5 . The computer-implemented method of  claim 1 , wherein each of the first machine-learning model, the second machine-learning model, and the third machine-learning model incorporate decision trees, random forests, extremely randomized trees, or gradient boosting trees. 
     
     
         6 . The computer-implemented method of  claim 1 , wherein the first set of features includes one or more of: a number of stimulation stages, fluid and/or volume thereof used during drilling, pressures applied during drilling, proppant type, proppant amount, completion type, or drilling equipment type. 
     
     
         7 . The computer-implemented method of  claim 1 , wherein the second set of features includes one or more of: type of geological formations, brittleness of rock, or porosity of rock. 
     
     
         8 . The computer-implemented method of  claim 1 , wherein the third set of features includes one or more of: horizontal spacing, vertical spacing, number of wells per the locations, or distance to a parent well. 
     
     
         9 . The computer-implemented method of  claim 1 , further comprising:
 based on use of the third machine-learning model, determining a scaling factor that represents a difference between attested per-well spacings and spacing values across the wells in the training data;   obtaining, from a pre-trained machine learning model, a predicted production for a new well given instances of the first set of features, the second set of features, and the third set of features as relating to the new well; and   modifying the predicted production by the scaling factor.   
     
     
         10 . The computer-implemented method of  claim 1 , wherein the spacings between wells are in terms of median spacing values. 
     
     
         11 . A computer-implemented method comprising:
 obtaining, from a first pre-trained machine-learning model, a predicted production for a new well given instances of a first set of features and a second set of features as relating to the new well, wherein the first pre-trained machine-learning model was trained with first set of features related to drilling of a set of wells and the second set of features related to respective geological properties of locations at which the wells were drilled, and wherein the predicted production represents a counterfactual spacing scenario specified during training of the first pre-trained machine-learning model;   obtaining, from a second pre-trained machine-learning model, a predicted scaling factor that represents a difference between attested per-well spacings and counterfactual spacing values across the wells represented in the first set of features and the second set of features used train the first pre-trained machine-learning model; and   modifying the predicted production by the predicted scaling factor.   
     
     
         12 . The computer-implemented method of  claim 11 , wherein a first additional machine-learning model was trained to predict a third set of features related to spacings between the wells given the first set of features and the second set of features, the computer-implemented method further comprising:
 determining a spacing error based on a difference between the third set of features used to train the first additional machine-learning model and as predicted by the first additional machine-learning model.   
     
     
         13 . The computer-implemented method of  claim 12 , wherein a second additional machine-learning model was trained to predict a fourth set of features relating to production output of the wells given the first set of features and the second set of features, the computer-implemented method further comprising:
 determining a production error based on a difference between the fourth set of features used to train the second additional machine-learning model and as predicted by the second additional machine-learning model.   
     
     
         14 . The computer-implemented method of  claim 13 , wherein a third additional machine-learning model was trained to predict the production error given the spacing error, the first set of features, and the second set of features, wherein the third set of features is not considered by the third additional machine-learning model, and wherein the predicted scaling factor is determined based on use of the third additional machine-learning model. 
     
     
         15 . The computer-implemented method of  claim 14 , wherein the production error is usable to estimate a further production output of wells with spacings not represented in the third set of features. 
     
     
         16 . The computer-implemented method of  claim 14 , wherein each of the first additional machine-learning model, the second additional machine-learning model, and the third additional machine-learning model incorporate decision trees, random forests, extremely randomized trees, or gradient boosting trees. 
     
     
         17 . The computer-implemented method of  claim 8 , wherein the spacings between wells are based on median spacing values. 
     
     
         18 . A non-transitory computer-readable medium, having stored thereon program instructions that, upon execution by a computing system, cause the computing system to perform operations comprising:
 obtaining, from a first pre-trained machine-learning model, a predicted production for a new well given instances of a first set of features and a second set of features as relating to the new well, wherein the first pre-trained machine-learning model was trained with first set of features related to drilling of a set of wells and the second set of features related to respective geological properties of locations at which the wells were drilled, and wherein the predicted production represents a counterfactual spacing scenario specified during training of the first pre-trained machine-learning model;   obtaining, from a second pre-trained machine-learning model, a predicted scaling factor that represents a difference between attested per-well spacings and counterfactual spacing values across the wells represented in the first set of features and the second set of features used train the first pre-trained machine-learning model; and   modifying the predicted production by the predicted scaling factor.   
     
     
         19 . The non-transitory computer-readable medium of  claim 18 , wherein the first set of features includes one or more of: a number of stimulation stages, fluid and/or volume thereof used during drilling, pressures applied during drilling, proppant type, proppant amount, completion type, or drilling equipment type, wherein the second set of features includes one or more of: type of geological formations, brittleness of rock, or porosity of rock, and wherein the third set of features includes one or more of: horizontal spacing, vertical spacing, number of wells per the locations, or distance to a parent well. 
     
     
         20 . The non-transitory computer-readable medium of  claim 18 , wherein the spacings between wells are based on median spacing values.