Optimizing wellbore operations for sustainability impact

A system can receive, at a user interface, at least one constraint and a range for at least one parameter for a wellbore operation. The system can generate, by at least one algorithm, a recommendation of a value for the at least one parameter within the range for the at least one parameter. The recommendation can be based on a sustainability metric and the at least one constraint for the wellbore operation. The system can output, at the user interface, the recommendation of the value for the at least one parameter and an indication of additional outcomes for the sustainability metric using other values within the range for the at least one parameter.

TECHNICAL FIELD

The present disclosure relates generally to wellbore operations and, more particularly, although not necessarily exclusively, to optimizing wellbore operations for sustainability impact.

BACKGROUND

Hydrocarbon exploration is the search for hydrocarbons, such as oil or gas, within a subterranean formation. Greenhouse gas emissions resulting from wellbore operations can impact the environment. A high carbon footprint or other sustainability metric for a wellbore operation can indicate inefficient processes or areas of the wellbore operation involving excessive resources. Determining a carbon footprint for a wellbore operation may be difficult due to a large amount of data to be analyzed. Additionally, different equipment and services used during a wellbore operation or multiple wellbore operations may have a different impact on the carbon footprint, so if the equipment or services are not known ahead of time, it may be difficult to determine the carbon footprint. And, even if the carbon footprint is determined, it is usually determined later than a time when adjustments can be made. Determining the carbon footprint earlier can prevent a high carbon footprint. Thus, understanding a carbon footprint for wellbore operations can provide significant value for efficient development of hydrocarbon resources.

DETAILED DESCRIPTION

Certain aspects and examples of the present disclosure relate to estimating sustainability impacts of a wellbore operation based on selected parameters and providing insights for decisions regarding wellbore operations to optimize a sustainability impact. The sustainability impact can include carbon emissions associated with the wellbore operation. By using a system according to some examples, sustainability impacts can be estimated for overall asset management of a reservoir. Some examples may allow parameters of a wellbore operation associated with reducing an environmental impact to be selected before the wellbore operation begins.

In some aspects, a user can be allowed to compare various outcomes from a series of scenarios for one or more wellbore operations. A user interface allows the user to build scenarios through the input of parameters and model constraints that are relevant to the asset being modeled. Examples of the parameters can include equipment or energy sources that are to be used during the wellbore operation. A range of possible parameter values to use for each parameter may be specified. A parameter value can correspond to a percentage for how much of the wellbore operation is associated with using the particular equipment or energy source. Examples of the constraints can include a budget, carbon footprint, or sustainability impact. Each constraint can be considered with regard to a weight that may be defined by the user. Constraints for the wellbore operation, such as a job size and a budget, can also be received. Then, a recommendation for a parameter value within the specified range for each parameter can be determined based on the constraints and a sustainability metric. Examples of the sustainability metric may include an amount of carbon emissions or a cost associated with the wellbore operation. The recommendation can be a parameter value associated with minimizing an environmental impact. The user interface presents those scenarios back to the user, showing the graphical relationship between the various constraints, allowing the user to choose from the recommendation and additional outcomes. The system can receive a modification to a selected scenario, such as receiving a change to the range of parameter values for the parameters, and present live updates to the scenario depending on which items are altered.

Once a scenario is selected, a predicted sustainability outcome associated with the scenario can be applied to future projections of an overall sustainability footprint using data from other, previously selected scenarios for other wellbore operations. The future projections may be compared to a sustainability target, and if the projections exceed the sustainability target, adjustments for the wellbore operation (or the other wellbore operations) may be determined to reduce the future projections. The adjustments can then be made to the corresponding scenario and updated future projections can be generated.

FIG.1is a block diagram of an example of a computing device100for implementing estimating sustainability impacts of wellbore operations according to one example of the present disclosure. The wellbore operations may be drilling operations, fracturing operations, completion operations, production operations, or a combination thereof. The computing device100can include a processor102, a bus106, a memory104, and a display device124. In some examples, the components shown inFIG.1can be integrated into a single structure. For example, the components can be within a single housing with a single processing device. In other examples, the components shown inFIG.1can be distributed (e.g., in separate housings) and in electrical communication with each other using various processors. It is also possible for the components to be distributed in a cloud computing system or grid computing system.

The processor102can execute one or more operations for estimating sustainability impacts for a wellbore operation. The processor102can execute instructions stored in the memory104to perform the operations. The processor102can include one processing device or multiple processing devices. Non-limiting examples of the processor102include a field-programmable gate array (“FPGA”), an application-specific integrated circuit (“ASIC”), a processor, a microprocessor, etc.

The processor102is communicatively coupled to the memory104via the bus106. The memory104may include any type of memory device that retains stored information when powered off. Non-limiting examples of the memory104include electrically erasable and programmable read-only memory (“EEPROM”), flash memory, or any other type of non-volatile memory. In some examples, at least some of the memory104can include a non-transitory medium from which the processor102can read instructions. A computer-readable medium can include electronic, optical, magnetic, or other storage devices capable of providing the processor102with computer-readable instructions or other program code. Non-limiting examples of a computer-readable medium include (but are not limited to) magnetic disk(s), memory chip(s), read-only memory (ROM), random-access memory (“RAM”), an ASIC, a configured processing device, optical storage, or any other medium from which a computer processing device can read instructions. The instructions can include processing device-specific instructions generated by a compiler or an interpreter from code written in any suitable computer-programming language, including, for example, C, C++, C #, etc.

In some examples, the computing device100includes a display device124. The display device124can represent one or more components used to output data. Examples of the display device124can include a liquid-crystal display (LCD), a computer monitor, a touch-screen display, etc. The display device124can include a user interface126for receiving inputs and displaying outputs associated with the wellbore operation.

The computing device100may include parameter(s)110describing the wellbore operation. The computing device100may receive the parameter(s)110as input from a user associated with the wellbore operation. The parameter(s)110can involve specified ranges for energy sources and equipment to be used during the wellbore operation. For example, the parameter(s) may involve ranges for using grid power, one or more dual fuel generators or engines, one or more natural gas or field gas generators, or one or more types of diesel engines. As one particular example, the parameter(s)110may include using grid power for between 20% and 100% of the wellbore operation. The computing device500can also receive constraint(s)112for the wellbore operation. For example, the constraint(s)112can include a budget for the wellbore operation and a hydraulic horsepower (HHP) for the wellbore operation. For example, the constraint(s)112can be a budget of $5,000,000 and a HHP of 20,000. The constraint(s)112may be input by the user. In some examples, the computing device100may additionally receive weights to be associated with each of the constraint(s)112. For example, the user may indicate that the budget is to be associated with a weight of 0.8 and the HHP is to be associated with a weight of 0.2.

The computing device100can execute a machine-learning model114using the parameter(s)110and the constraint(s) as inputs. The computing device100can use the machine-learning model114to determine a recommendation116of a value to use for the parameter(s)110within the range of values specified for each of the parameter(s)110. In addition to the machine-learning model114, the computing device100may use one or more additional algorithms, such as a non-linear optimization, to generate the recommendation116. The recommendation116may be based on a sustainability metric118for the wellbore operation. For example, the sustainability metric118may be emissions or cost, so the recommendation116can be a value for each of the parameter(s)110for minimizing the emissions or the cost. Training data can be used to train a neural network for selecting the recommendation116. In an example, the training data can be historical data associated with parameters and constraints for wellbore operations. The training data may be labeled with a resulting value for the emissions or cost associated with using particular parameters. If weights are specified for the constraint(s)112, the machine-learning model114can use the weights to determine the recommendation116.

The computing device100can also determine additional outcomes120for the sustainability metric118if other values within the specified ranges are used for the parameter(s)110. For example, the computing device100may generate a graph of outcomes that includes the recommendation116and the additional outcomes120. A user may then select the recommendation116, or one of the additional outcomes120for the wellbore operation. Based on the selection, the computing device100can generate a predicted sustainability outcome128. The predicted sustainability outcome128can include the sustainability impact resulting from the selection, as well as the sustainability impact resulting from selections for additional wellbore operations. For example, multiple wellbore operations may be associated with an entity. A user can input parameter(s)110and constraint(s)112for each of the multiple wellbore operations, receive a recommendation for value(s) for the parameter(s)110for each of the multiple wellbore operations, and select either the recommendation or an additional outcome for each of the multiple wellbore operations. The computing device100can then combine the sustainability impact for the multiple wellbore operations based on the selections to determine a predicted sustainability outcome128for the entity. The computing device100can compare the predicted sustainability outcome128to a sustainability target, such as for a carbon footprint, for the entity. If the computing device100determines that the predicted sustainability outcome128exceeds the sustainability target, the computing device100can determine an adjustment for one or more of the wellbore operations so that the predicted sustainability outcome128meets the sustainability target.

In some examples, the computing device100can include an action module122that can take the predicted sustainability outcome128and apply it to some other process. For example, the computing device100can use the predicted sustainability outcome128to control a well drilling operation, a well completion operation, or some other process relevant to the predicted sustainability outcome128. The action module122can apply the predicted sustainability outcome128to develop a plan for drilling operations, completion operations, or production operations. In some examples, the computing device100can implement the process200or the process300shown inFIGS.2-3for effectuating some aspects of the present disclosure. Other examples can involve more operations, fewer operations, different operations, or a different order of the operations shown inFIGS.2-3. The operations ofFIGS.2-3are described below with reference to the components shown inFIG.1.

Referring toFIG.2, in block202, the processor102can receive, at a user interface126, at least one constraint112and a range for at least one parameter110for a wellbore operation. For example, a user can input a budget, carbon emissions, or other suitable constraints for the wellbore operation. Additionally, the user can input a range of values for equipment and energy source usage for the wellbore operation. In some example, the processor102can also receive weights for the at least one constraint112.

In block204, the processor102can generate, by a machine-learning model114, a recommendation116of a value for the at least one parameter110within the range for the at least one parameter110. The recommendation116can be based on a sustainability metric118, such as carbon emissions or cost, and the at least one constraint112for the wellbore operation. For example, the machine-learning model114can output a recommendation116of using 49% grid power, 28% tier 4 diesel engines, and 23% tier 2 diesel engines for the wellbore operation to achieve a balance between a lowest cost and a lowest emissions for the wellbore operation.

In block206, the processor102can output, at the user interface126, the recommendation116of the value for the at least one parameter110and an indication of additional outcomes120for the sustainability metric118using other values within the range for the at least one parameter110. The user interface126may display the recommendation116and the additional outcomes120in a graph that shows parameter values to use for achieving different values for the sustainability metric.

In block304, the processor102can run algorithms. The algorithms can include the machine-learning model114, along with other models, such as non-linear optimization. The algorithms can determine a recommendation116for the wellbore operation.

In block306, the processor102can present a visualization of scenarios and sustainability impact. Each of the scenarios can include different parameter values for the parameter(s). The visualization may include an indication of the scenario associated with the recommendation116.

In block308, the processor102can modify constraint balance. For example, the processor102can receive an indication of weights are to be applied for the constraint(s)112. The processor102can then return to block302to repeat blocks302through306using the weights.

In block310, the processor102saves the scenario. The scenario may be the scenario associated with the recommendation116, or a different scenario selected by a user. The scenario can be saved local to the computing device100or remote from the computing device100in a location that is accessible by the computing device100.

In block312, the processor102can apply sustainability metrics to projections of future impacts. For example, a predicted sustainability outcome128associated with the scenario can be added with predicted sustainability outcomes for other selected scenarios. The resulting sustainability impact can indicate a projected impact at a future point in time.

In block314, the processor102can output insights on an effect to overall sustainability goals. For example, the processor102can determine how the predicted sustainability outcome128effects a sustainability target. The processor102may determine an adjustment for one or more wellbore operations if the predicted sustainability outcome128exceeds the sustainability target.

FIG.4is an example of a user interface426for inputting parameters410and constraints412for a wellbore operation according to one example of the present disclosure. The user interface426includes sliders for indicating ranges for the parameters410. Other examples may include text boxes for receiving numerical inputs for the ranges for the parameters410. The parameters410are shown to include equipment usage ranges for dual fuel generators, grid power, tier 2 diesel engines, and tier 4 diesel engines. For example, the parameters410show a ranges of 0% to 20% for duel fuel generators, 18% to 100% for grid power, 15 to 20% for tier 2 diesel engines, and 0% to 20% for tier 4 diesel engines. The user interface426also includes drop-down menus for selecting the constraints412for the wellbore operation. The constraints412illustrated a job size, indicated by an HHP value, and a budget. For example, the constraints412show a selection of a medium job size corresponding to20,000HHP and a budget of $5,000,000. The user interface426includes a button that can be selected for generating a recommendation for the parameters410based on the constraints412.

FIG.5is an example of a user interface526presenting recommended parameter values for a wellbore operation according to one example of the present disclosure. The user interface526may be below the user interface426inFIG.4subsequent to the parameters and constraints being selected. Parameter values and associated costs and emissions are presented as a graph530. A recommendation516on the graph530can be parameter values that balance sustainability metrics (e.g., emissions and cost) for the wellbore operation. For example, the user interface526shows possible outcomes that minimize emissions and cost, and the recommendation516balances the emissions and the cost. As illustrated, the recommendation516involves using 49% grid power, 28% tier 4 diesel engines, and 23% tier 2 diesel engines. The recommended scenario is shown to result in an estimated emissions of 525.8 tCO2e and an estimate cost of $2,176,171. A user may scroll over any portion of the graph530to see associated parameter values, emissions, and cost. A user may select any point along the graph530to save as the selected parameter values for the wellbore operation.

FIG.6is an example of a user interface626for presenting estimated sustainability impacts of a wellbore operation according to one example of the present disclosure. The user interface626may receive parameters610from a user to use for a wellbore operation. The parameters610can include equipment to use during the wellbore operation. For example, the user can select drilling profile of a primary rig and a spudder rig for a drilling operation. The user interface626may also receive a selection for a completion profile of a fracturing spread for a completion operation. The user interface626can present a recommendation616, and a graph showing emissions over time associated with using the recommendation616. Additionally, the graph can show emissions over time associated with using parameters associated with a lowest cost scenario and a lowest emissions scenario. The user can then select a scenario to use for the wellbore operation.

FIG.7is an example of a user interface726for presenting estimated sustainability impacts of multiple wellbore operations according to one example of the present disclosure. The user interface726can display a sustainability target732associated with carbon intensity for wellbore operations and a predicted sustainability outcome728resulting from the wellbore operations. The user interface726illustrates five wellbore operations that contribute to the predicted sustainability outcome728and a contribution of each wellbore operation to the predicted sustainability outcome728. A user may determine an adjustment to be made to one or more of the wellbore operations based on the predicted sustainability outcome728exceeding the sustainability target732. Alternatively, the user interface726may present a recommended adjustment for a wellbore operation to cause the predicted sustainability outcome728to meet the sustainability target732.

FIG.8a schematic of a well system800associated with an estimated sustainability impact according to one example of the present disclosure. The well system800can include a wellbore802extending through various earth strata. The wellbore802can extend through a subterranean formation804that can include hydrocarbon material such as oil, gas, coal, or other suitable material. In some examples, a casing string806can extend from a well surface822into the subterranean formation804. The casing string806can provide a conduit through which formation fluids, such as production fluids produced from the subterranean formation804, can travel to the well surface822. The casing string806can be coupled to walls of the wellbore802via cement or other suitable coupling material. For example, a cement sheath808can be positioned or formed between the casing string806and the walls of the wellbore802for coupling the casing string806to the wellbore802. The casing string806can be coupled to the wellbore802using other suitable techniques.

The well system800can include at least one well tool, such as a well tool810. The well tool810can be coupled to a wireline814, a slickline, or a coiled tube that can be deployed into the wellbore802. The wireline814, the slickline, or the coiled tube can be guided into the wellbore802using, for example, a guide or winch. In some examples, the wireline814, the slickline, or the coiled tube can be unwound from around a reel to be deployed into the wellbore802.

A computing device840can be positioned at the surface822of the well system800. In some examples, the computing device840can be positioned downhole in the wellbore802, remote from the well system800, or in other suitable locations with respect to the well system800. The computing device840can be communicatively coupled to the well tool810or other suitable components of the well system800via a wired or wireless connections. For example, as illustrated inFIG.8, the computing device840can include an antenna842that can allow the computing device840to receive and to send communications relating to the well system800. The computing device840may be in communication with another computing device, such as the computing device100inFIG.1, and can receive commands to adjust aspects of the well system800based on a determined sustainability impact associated with the well system800. For example, the commands may adjust equipment usage or other aspects for the well system800.

AlthoughFIG.8is shown as a completion environment, the well system800may alternatively be associated with a drilling operation, a fracturing operation, or a production operation. In each wellbore operation, the computing device840may receive commands to adjust the operation based on the predicted sustainability outcome.

In some aspects, a system, a method, or a non-transitory computer-readable medium for estimating sustainability impacts according to one or more of the following examples:

A system comprising a processing device; and a memory device that includes instructions executable by the processing device for causing the processing device to perform operations comprising: receiving, at a user interface, at least one constraint and a range for at least one parameter for a wellbore operation; generating, by at least one algorithm, a recommendation of a value for the at least one parameter within the range for the at least one parameter, the recommendation being based on a sustainability metric and the at least one constraint for the wellbore operation; and outputting, at the user interface, the recommendation of the value for the at least one parameter and an indication of additional outcomes for the sustainability metric associated with using other values within the range for the at least one parameter.

Example 2 is the system of example 1, wherein the memory device further includes instructions executable by the processing device for causing the processing device to: receive a weight to be associated with each constraint of the at least one constraint; and generate the recommendation based on the weight associated with each constraint of the at least one constraint.

Example 3 is the system of example(s) 1-2, wherein the memory device further includes instructions executable by the processing device for causing the processing device to: receive a selection of the recommendation for the wellbore operation; and generate a predicted sustainability outcome based on the selection and additional selections for additional wellbore operations.

Example 4 is the system of example(s) 1-3, wherein the selection is a first selection and the memory device further includes instructions executable by the processing device for causing the processing device to: receive a second selection of an additional outcome of the additional outcomes; and generate an updated predicted sustainability outcome based on the second selection and the additional selections for the additional wellbore operations.

Example 5 is the system of example 1-4, wherein the memory device further includes instructions executable by the processing device for causing the processing device to: compare the predicted sustainability outcome to a sustainability target; determine the predicted sustainability outcome exceeds the sustainability target; and determine an adjustment for the wellbore operation or the additional wellbore operations to cause the predicted sustainability outcome to meet the sustainability target.

Example 6 is the system of example(s) 1-5, wherein the at least one algorithm comprises a machine-learning model, a non-linear optimization, or a combination thereof.

Example 7 is the system of example(s) 1-6, wherein the memory device further includes instructions executable by the processing device for causing the processing device to output the recommendation by: displaying, at the user interface, a graph associated with the wellbore operation, the graph including the recommendation and the additional outcomes; presenting the graph on the user interface below the at least one parameter and the at least one constraint, and displaying an indication on the graph at a position corresponding to the recommendation.

Example 8 is a method comprising receiving, at a user interface, at least one constraint and a range for at least one parameter for a wellbore operation; generating, by at least one algorithm, a recommendation of a value for the at least one parameter within the range for the at least one parameter, the recommendation being based on a sustainability metric and the at least one constraint for the wellbore operation; and outputting, at the user interface, the recommendation of the value for the at least one parameter and an indication of additional outcomes for the sustainability metric associated with using other values within the range for the at least one parameter.

Example 9 is the method of example 8, further comprising: receiving a weight to be associated with each constraint of the at least one constraint; and generating the recommendation based on the weight associated with each constraint of the at least one constraint.

Example 10 is the method of example(s) 8-9, further comprising: receiving a selection of the recommendation for the wellbore operation; and generating a predicted sustainability outcome based on the selection and additional selections for additional wellbore operations.

Example 11 is the method of example(s) 8-10, wherein the selection is a first selection and the method further comprises: receiving a second selection of an additional outcome of the additional outcomes; and generating an updated predicted sustainability outcome based on the second selection and the additional selections for the additional wellbore operations.

Example 12 is the method of example(s) 8-11, further comprising: comparing the predicted sustainability outcome to a sustainability target; determining the predicted sustainability outcome exceeds the sustainability target; and determining an adjustment for the wellbore operation or the additional wellbore operations to cause the predicted sustainability outcome to meet the sustainability target.

Example 13 is the method of example(s) 8-12, wherein the at least one algorithm comprises a machine-learning model, a non-linear optimization, or a combination thereof.

Example 14 is the method of example(s) 8-13, wherein outputting the recommendation comprises: displaying, at the user interface, a graph associated with the wellbore operation, the graph including the recommendation and the additional outcomes; presenting the graph on the user interface below the at least one parameter and the at least one constraint, and displaying an indication on the graph at a position corresponding to the recommendation.

Example 15 is a non-transitory computer-readable medium comprising instructions that are executable by a processing device for causing the processing device to perform operations comprising receiving, at a user interface, at least one constraint and a range for at least one parameter for a wellbore operation; generating, by at least one algorithm, a recommendation of a value for the at least one parameter within the range for the at least one parameter, the recommendation being based on a sustainability metric and the at least one constraint for the wellbore operation; and outputting, at the user interface, the recommendation of the value for the at least one parameter and an indication of additional outcomes for the sustainability metric associated with using other values within the range for the at least one parameter.

Example 16 is the non-transitory computer-readable medium of example 15, further comprising instructions executable by the processing device for causing the processing device to: receive a weight to be associated with each constraint of the at least one constraint; and generate the recommendation based on the weight associated with each constraint of the at least one constraint.

Example 17 is the non-transitory computer-readable medium of example(s) 15-16, further comprising instructions executable by the processing device for causing the processing device to: receive a selection of the recommendation for the wellbore operation; and generate a predicted sustainability outcome based on the selection and additional selections for additional wellbore operations.

Example 18 is the non-transitory computer-readable medium of example(s) 15-17, wherein the selection is a first selection and further comprising instructions executable by the processing device for causing the processing device to: receive a second selection of an additional outcome of the additional outcomes; and generate an updated predicted sustainability outcome based on the second selection and the additional selections for the additional wellbore operations.

Example 19 is the non-transitory computer-readable medium of example(s) 15-18, further comprising instructions executable by the processing device for causing the processing device to: compare the predicted sustainability outcome to a sustainability target; determine the predicted sustainability outcome exceeds the sustainability target; and determine an adjustment for the wellbore operation or the additional wellbore operations to cause the predicted sustainability outcome to meet the sustainability target.

Example 20 is the non-transitory computer-readable medium of example(s) 15-19, further comprising instructions executable by the processing device for causing the processing device to output the recommendation by: displaying, at the user interface, a graph associated with the wellbore operation, the graph including the recommendation and the additional outcomes; presenting the graph on the user interface below the at least one parameter and the at least one constraint, and displaying an indication on the graph at a position corresponding to the recommendation.