Analyzing and enhancing performance of oilfield assets

1 . A method, comprising:

receiving oilfield data input for a plurality of wells comprising a first well and a second well, the oilfield data input comprising at least one of total proppant mass, total fluid volume, true vertical depth, total depth, elevation, lateral length, latitude, longitude, first month production, or initial decline rate of production;

projecting a production for the plurality of wells at least in part on the oilfield data input and geological parameters using Rate Transient Analysis (RTA) having an embedded machine learning model (MLM), wherein using the RTA comprises optimizing the embedded MLM using a regression model;

aggregating the production for the plurality of wells by generating a production forecasting MLM incorporating individual forecast models including a separate neural network for each month of the production separately for the first well and the second well and arranging each individual forecast model together to construct a time series model configured to project the production;

ranking the first well and the second well including assigning a ranking to the first well based on the projected production;

completing the first well based on the ranking;

extracting hydrocarbons from the first well at a production rate via hydraulic fracturing using a drilling tool;

automatically adjusting, by a controller and based on the time series model, the production rate to increase an efficiency of the drilling tool;

identifying one or more wells of the plurality of wells including at least one of the first well and the second well for additional completion operations;

determining one or more completion operations to conduct for the one or more wells, wherein the determining the one or more completion operations uses the individual forecast models to forecast results of the one or more completion operations;

computing, for the one or more wells, generational analytics related to well behaviors and based on respective well ages; and

displaying, for the one or more wells, at least one of the generational analytics, recommendations, or projections using a display dashboard.

2 . The method of claim 1 , wherein the forecast results comprise at least one of a total proppant mass of the hydraulic fracturing, a total fluid volume of the hydraulic fracturing, a proppant mass per foot of lateral length of the hydraulic fracturing, or a fluid in gals per foot of lateral length of the hydraulic fracturing.

3 . The method of claim 2 , wherein the displaying includes displaying the recommendations, wherein the recommendations include the at least one of the total proppant mass of the hydraulic fracturing, the total fluid volume of the hydraulic fracturing, the proppant mass per foot of lateral length of the hydraulic fracturing, or the fluid in gals per foot of lateral length of the hydraulic fracturing.

4 . The method of claim 1 , wherein the plurality of wells includes at least one not yet drilled well.

5 . The method of claim 4 , wherein the displaying includes displaying the projections, wherein the projections include a long-term cashflow for the at least one not yet drilled well.

6 . The method of claim 1 , wherein the determining includes considering a connectivity of two or more proximal wells of the one or more wells.

7 . The method of claim 1 , wherein constructing the time series model configured to project the production comprises projecting at least 72 months of the production.

8 . The method of claim 1 , wherein displaying the at least one of the generational analytics, the recommendations, or the projections comprises displaying, via the display dashboard, the adjusting, the production rate, and the efficiency of the drilling tool.

9 . A non-transitory, computer-readable medium storing instructions that, when executed by at least one processor of a computing system, cause the computing system to perform operations, the operations comprising:

receiving oilfield data input for a plurality of wells comprising a first well and a second well, the oilfield data input comprising at least one of total proppant mass, total fluid volume, true vertical depth, total depth, elevation, lateral length, latitude, longitude, first month production, or initial decline rate of production;

projecting a production for the plurality of wells at least in part on the oilfield data input and geological parameters using Rate Transient Analysis (RTA) having an embedded machine learning model (MLM), wherein using the RTA comprises optimizing the embedded MLM using a regression model;

aggregating the production for the plurality of wells by generating a production forecasting MLM incorporating individual forecast models including a separate neural network for each month of production separately for the first well and the second well and arranging each individual forecast model together to construct a time series model configured to project the production;

ranking the first well and the second well including assigning a ranking to the first well based on the projected production;

completing the first well based on the ranking;

extracting hydrocarbons from the first well at a production rate via hydraulic fracturing using a drilling tool;

automatically adjusting, based on the time series model, the production rate to increase an efficiency of the drilling tool;

identifying one or more wells of the plurality of wells including at least one of the first well and the second well for additional completion operations;

determining one or more completion operations to conduct for the one or more wells, wherein the determining the one or more completion operations includes considering a connectivity of two or more proximal wells of the identified wells and uses the individual forecast models to forecast results of the one or more completion operations;

computing, for the one or more wells, at least one of generational analytics related to well behaviors and based on respective well ages; and

displaying, for the one or more wells, the generational analytics, recommendations, or projections using a display dashboard.

10 . The non-transitory, computer-readable medium of claim 9 , wherein the forecast results comprise at least one of a total proppant mass of the hydraulic fracturing, a total fluid volume of the hydraulic fracturing, a proppant mass per foot of lateral length of the hydraulic fracturing, or a fluid in gals per foot of lateral length of the hydraulic fracturing.

11 . The non-transitory, computer-readable medium of claim 10 , wherein the displaying includes displaying the recommendations, wherein the recommendations include the at least one of the total proppant mass of the hydraulic fracturing, the total fluid volume of the hydraulic fracturing, the proppant mass per foot of lateral length of the hydraulic fracturing, or the fluid in gals per foot of lateral length of the hydraulic fracturing.

12 . The non-transitory, computer-readable medium of claim 11 , wherein the determining includes considering a connectivity of two or more proximal wells of the one or more wells.

13 . The non-transitory, computer-readable medium of claim 9 , wherein the plurality of wells includes at least one not yet drilled well.

14 . A computing system comprising:

one or more processors; and

a memory system including one or more non-transitory, computer-readable media storing instructions that, when executed by at least one of the one or more processors, cause the computing system to perform operations, the operations including:

receiving oilfield data input for a plurality of wells comprising a first well and a second well, the oilfield data input comprising at least one of total proppant mass, total fluid volume, true vertical depth, total depth, elevation, lateral length, latitude, longitude, first month production, or initial decline rate of production;

projecting a production for the plurality of wells at least in part on the oilfield data input and geological parameters using Rate Transient Analysis (RTA) having an embedded machine learning model (MLM), wherein using the RTA comprises optimizing the embedded MLM using a regression model;

aggregating the production for the plurality of wells by generating a production forecasting MLM incorporating individual forecast models including a separate neural network for each month of the production separately for the first well and the second well and arranging each individual forecast model together to construct a time series model configured to project the production;

ranking the first well and the second well including assigning a ranking to the first well based on the projected production;

completing the first well based on the ranking;

extracting hydrocarbons from the first well at a production rate via hydraulic fracturing using a drilling tool;

automatically adjusting, based on the time series model, the production rate to increase an efficiency of the drilling tool;

identifying one or more wells of the plurality of wells including at least one of the first well and the second well for additional completion operations;

determining one or more completion operations to conduct for the one or more wells, wherein the determining the one or more completion operations includes considering connectivity of two or more proximal wells of the identified wells and uses the individual forecast models to forecast results of the one or more completion operations;

computing, for the one or more wells, at least one of generational analytics related to well behaviors and based on respective well ages; and

displaying, for the one or more wells, the generational analytics, recommendations, or projections using a display dashboard.

15 . The computing system of claim 14 , wherein the forecast results comprise at least one of a total proppant mass of the hydraulic fracturing, a total fluid volume of the hydraulic fracturing, a proppant mass per foot of lateral length of the hydraulic fracturing, or a fluid in gals per foot of lateral length of the hydraulic fracturing.

16 . The computing system of claim 15 , wherein the displaying includes displaying the recommendations, wherein the recommendations include the at least one of the total proppant mass of the hydraulic fracturing, the total fluid volume of the hydraulic fracturing, the proppant mass per foot of lateral length of the hydraulic fracturing, or the fluid in gals per foot of lateral length of the hydraulic fracturing.

17 . The computing system of claim 14 , wherein the determining includes considering connectivity of two or more proximal wells of the one or more wells.

18 . The computing system of claim 14 , wherein the plurality of wells includes at least one not yet drilled well.