BACKGROUND

The operation of an oil and gas production unit involves making various decisions which affect the production volume and the quality of the blend produced. There are different approaches to gas blend optimization. While some existing approaches seek to enhance a current production, they cannot be used to optimize future targets before putting wells on production, or for planning special production scenarios beforehand.

Accordingly, there exists a need for a system and method that optimizes the quality of the final sales gas prior to putting the wells on production.

SUMMARY

This summary is provided to introduce a selection of concepts that are further described below in the detailed description. This summary is not intended to identify key or essential features of the claimed subject matter, nor is it intended to be used as an aid in limiting the scope of the claimed subject matter.

In one aspect, embodiments disclosed herein relate to a system for gas blend optimization. The system includes an analytical module equipped to obtain well data from a plurality of wells within a geographical region of interest and one or more computer processors and a non-transitory computer-readable medium having executable instructions encoded thereon such that when executed, the one or more processors perform multiple operations. The operations include obtaining a target rate for one or more parameters of interest in a desired gas blend and obtaining data related to the one or more parameters of interest for each well of a plurality of wells within the geographical region of interest. Based on the obtained data, an effect of each well's contribution to the one or more parameters of interest in the total gas produced at a gas processing facility is determined. Based on the identified effect of each well, a list of wells in the plurality of wells is selected. Finally, the one or more parameters of interest in the total gas produced are optimized until the assigned target rate is met by adjusting a well gas rate for each well in a list of wells based on their effects on the total gas produced.

In another aspect, embodiments disclosed herein related to a computer implemented method and a computer readable program. The computer readable program includes computer-readable instructions stored on a non-transitory computer-readable medium that are executable by a computer having one or more computer processors, such that upon execution of the instructions, the one or more computer processors perform the operations listed herein. The computer implemented method includes an act of causing a computer to execute such instructions and perform the resulting operations.

Other aspects and advantages of the claimed subject matter will be apparent from the following description and the appended claims.

BRIEF DESCRIPTION OF DRAWINGS

Specific embodiments of the disclosed technology will now be described in detail with reference to the accompanying figures. Like elements in the various figures are denoted by like reference numerals for consistency. This disclosed technology may, however, be embodied in many different forms and should not be construed as limited to the implementations set forth herein. The components described hereinafter as making up various elements of the disclosed technology are intended to be illustrative and not restrictive. Many suitable components that would perform the same or similar functions as components described herein are intended to be embraced within the scope of the disclosed electronic devices and methods. Such other components not described herein may include, but are not limited to, for example, components developed after development of the disclosed technology.

FIGS. 1-3 illustrate systems according to some embodiments of the present disclosure;

FIG. 4 is a flowchart illustrating a method for gas blend optimization according to some embodiments of the present disclosure;

FIG. 5 illustrates a summarized output of an optimized gas blend according to some embodiments of the present disclosure; and

FIG. 6 illustrates a computing system according to embodiments of the present disclosure.

DETAILED DESCRIPTION

In the following detailed description of embodiments of the disclosure, numerous specific details are set forth in order to provide a more thorough understanding of the disclosure. However, it will be apparent to one of ordinary skill in the art that the disclosure may be practiced without these specific details. In other instances, well-known features have not been described in detail to avoid unnecessarily complicating the description.

Throughout the application, ordinal numbers (e.g., first, second, third, etc.) may be used as an adjective for an element (i.e., any noun in the application). The use of ordinal numbers is not to imply or create any particular ordering of the elements nor to limit any element to being only a single element unless expressly disclosed, such as using the terms “before”, “after”, “single”, and other such terminology. Rather, the use of ordinal numbers is to distinguish between the elements. By way of an example, a first element is distinct from a second element, and the first element may encompass more than one element and succeed (or precede) the second element in an ordering of elements.

It is also to be understood that the mention of one or more method steps does not preclude the presence of additional method steps or intervening method steps between those steps expressly identified. Similarly, it is also to be understood that the mention of one or more components in a device or system does not preclude the presence of additional components or intervening components between those components expressly identified. Further, steps of the method may be performed in parallel or in sequence, and steps may be omitted or added without departing from the scope herein.

In one aspect, embodiments disclosed herein relate to a system and method for optimizing a gas blend based on a criterion defined by the user. A typical refinery has many different hydrocarbon streams to consider as blend stocks. Gasoline may be blended to meet multiple different quality specifications, such as vapor pressure; initial, intermediate, and final boiling points; carbon dioxide (CO₂) content, sulfur (e.g., hydrogen sulfide (H₂S)) content, chloride content, nitrogen gas (N₂₊) content; color; stability; aromatics content; olefin content; octane measurements; and other local governmental or market restrictions.

The system and method described herein ensures operational limits are met while minimizing the impact of undesired levels of certain parameters by analyzing the performance of each well based on an assigned well target. A well target is the availed gas production that needs to be met in a specified month. The target may be adjusted for various reasons, including energy demand or a change in reservoir depletion strategy. The assigned rate per well is related directly to the depletion strategy of the field and may vary in rate from thousands of cubic feet of gas to millions of cubic feet of gas per day. Furthermore, certain processes in gas separation units require having a specific CO₂/H₂S ratio. A significant deviation from a specific ratio may affect the integrity of the equipment. Additionally, maintaining specific levels of parameters, such as H₂S and CO₂, in the blended gas is essential for environmental, operational, and health reasons.

The system and method according to embodiments of the present disclosure generates an optimized gas blend that meets a specified target that may be set by a user. The optimized gas blend is generated by automatically altering the assigned target of the wells with minimum user interaction so that the rates of all wells (i.e., total well rate) may be adjusted. This adjustment is determined based on the control and measurements of the raw gas at both the well level and the gas plant level, as described in detail below. The final gas blend is optimized by a wide range of parameters that include, but are not limited to, CO₂, H₂S, chloride, and N₂₊.

FIG. 1 shows a schematic diagram in accordance with one or more embodiments. As shown in FIG. 1, a natural gas production and processing plant management network (e.g., plant management network (100)) may include various plant processing facilities (e.g., gas processing facility (102)), such as a gas-oil separation plant (GOSP), a gas plant, a refinery, or another type of plant. The plant management network may include various user devices (e.g., user device (106)), various servers, and various network elements (not shown). Plant facilities may include plant equipment (e.g., plant device (108)) and various control systems (e.g., control system (110)). Plant equipment may be used to perform one or more plant processes, such as gas operations, unconventional resources operations, refining operations, and natural gas liquid (NGL) fractionation. The plant management network A (100) may be similar to network (712) described below in FIG. 7 and the accompanying description. User devices (106) may include personal computers, handheld computer devices such as a smartphone or personal digital assistant, or a human machine interface (HMI). Plant devices (108) may include storage tanks, heat exchangers, accumulators, boilers, pumps, inlet separators, coolers, evaporators, plant sensors, plant instruments, gauges, control switches, valves, emergency stop controls, pressure relief equipment, flaring equipment, smoke detectors, toxic gas detectors, thermal detectors, combustible gas detectors, electric power generators, turbines, exhaust fans, light panels, fume scrubbers, safety showers, and other plant equipment.

The system may also include multiple well environments (e.g., well environment A (112), well environment B (114)) in a geographical region of interest near the gas processing facility (102), which may be similar to the well environment described below in FIG. 2 and the accompanying description. Further, the system may include a gas blend optimizer (116) in electronic communication with one or more well environments (112, 114), which is configured to collect one or more of well data, user data, and plant data in performing optimization of a desired gas blend. The gas blend optimizer (116) may include machine learning algorithms (118) and well log data (120). Machine learning algorithms (118) may include, but are not limited to, supervised learning, unsupervised learning, reinforcement learning, linear regression, logistic regression, k-Means, k-Nearest Neighbors (kNN), decision tree, support vector machine (SVM), Naïve Bayes, and gradient boosting algorithms.

The well log data (120) may include any data related to visual observations while drilling or data generated by instruments lowered into the well during drilling. Well log data (120) is used to measure, for instance, depths of formation tops, thickness of formations, porosity, water saturation, temperature, estimated permeability, and presence of oil and/or gas.

The gas blend optimizer (116) may include hardware and/or software with functionality for generating one or more machine learning models (122) using the machine learning algorithms (118). Thus, different types of machine-learning models may be trained, such as convolutional neural networks, deep neural networks, recurrent neural networks, support vector machines, decision trees, inductive learning models, deductive learning models, supervised learning models, unsupervised learning models, reinforcement learning models, etc. In some embodiments, two or more different types of machine-learning models are integrated into a single machine-learning architecture, e.g., a machine learning model may include decision trees and neural networks. In some embodiments, the gas blend optimizer (116) may generate augmented or synthetic data to produce a large amount of interpreted data for training a particular model.

FIG. 2 shows a schematic diagram in accordance with one or more embodiments. FIG. 2 illustrates one embodiment of a well environment (112, 114) that may include a well (200) having a wellbore (202) extending into a formation (204). The wellbore (202) may include a bored hole that extends from the surface into a target zone of the formation (204), such as a reservoir. The formation (204) may include various formation characteristics of interest, such as formation porosity, formation permeability, resistivity, density, water saturation, and the like. An analytical module (206) is equipped with a variety of sensors/meters (208) and gas analyzers that may be configured to obtain/measure (or collect) a variety of parameters from well sample data, including one or more of pH, CO₂, conductivity, chlorine, dissolved oxygen, turbidity, oxidation-reduction potential (ORP), flow rate, and chemicals (e.g., iron, hardness, nitrates, phosphates, ammonium, aluminum, chromate, copper, hydrazine, manganese, silicate, H₂S). Oil and gas sensors (or meters) (208) may be used for remote monitoring, condition monitoring, analysis, and simulations near oil and gas wells and pipelines. A venturi tube (or venturi meter) or other flow meters may be utilized to measure the flow rate of liquids at each well.

The well sample data obtained at the surface and sensor data may then be analyzed on a gas-chromatograph equipped with a mass-spectrometer (210), and processing may be performed at a processing module (212). The processing module (212) may be implemented in hardware (i.e., circuitry), software, or any combination thereof. In one embodiment, the GC-MS (210) is a triple quadrupole mass spectrometer, or GC-MS-QQQ. The GC-MS-QQQ is a tandem mass spectrometer consisting of two quadrupole mass analyzers in series with a radio frequency (RF)-only quadrupole between them to act as a cell for collision-induced dissociation. The GC-MS-QQQ allows for increased sensitivity and specificity, resulting in lower detection and quantitation limits. As can be appreciated by one skilled in the art, analysis equipment other than GC-MS may be utilized to analyze well and gas data.

FIG. 3 depicts a schematic diagram of a natural gas pipeline. Specifically, FIG. 3 illustrates gathering pipelines (300) collecting gas from well environment A (112) and well environment B (114) and directing the gas to a processing facility (302). The gas processing facility (102) processes the gas collected from the well environments (112, 114) and delivers a final gas blend that may be distributed to residential, commercial, and industrial consumers.

Referring again to FIG. 2, the gas blend optimizing system described herein needs access to current production readings (i.e., well log data (120)) and/or planned gas targets for all wells in well environments, such as the well environment (112, 114) depicted in FIG. 2). Embodiments disclosed herein utilizes an intermittent (i.e., not continuous) sampling of the gas produced by each well (i.e., well sample data) at the wellhead before it gets mixed with other wells; thus, a continuous measure of various gas components is not required. The well sample data may then be analyzed by the GC-MS (210) to obtain molar constituents (e.g., mole percent of parameters), such as measures of oxygen and/or N₂at a given well. Mole percent is the percentage of the total moles of a particular component (e.g., oxygen, N₂). Additionally, a summary of well properties, such as CO₂, water rate, and H₂S content per well environment is needed. As can be appreciated by one skilled in the art, there are a variety of sensors (208) that may be positioned in a wellbore (202) and at the ground surface near the well (200) to collect the data related to well properties of interest.

Referring to FIGS. 1-2, in some embodiments, the gas blend optimizer (116) is in communication with at least one of the analytical module (206) and a gas processing facility (102) that may be used to record and process well and production data. A control system (112) may obtain data regarding the wells as well as transmit one or more commands to components that may adjust one or more parameters. In some embodiments, the control system (112) may also include functionality for predicting a future production rate. In some embodiments, the control system includes a computer system that is the same as or similar to that of computer system 700 described below in FIG. 7 and the accompanying description. In some embodiments, the obtained data may be linked with a business intelligence (BI) application (e.g., Spotfire, Microsoft BI, Tableau) that supports a programming language (e.g., Python, R) to perform advanced data computation and manipulation.

The gas blend optimization workflow according to embodiments of the present disclosure comprises multiple steps. In an initial step, the system performs data importing and linking, as described above. The data that is imported may be well log data (120), data from the processing module (212), or data from an external source (e.g., database). An optimization method is selected to adjust the target rates for all wells within a gas processing plant, or for wells sharing a common factor (e.g., gas gathering manifold). Many optimization methods are available to one of ordinary skill in the art for optimizing the target rates. Non-limiting examples of optimization methods include the steepest decent method, the stochastic gradient descent method, the nonlinear conjugate gradient method, the Broyden-Fletcher-Goldfarb-Shannon (BFGS) method, the limited-memory BFGS method, and simulated annealing.

The optimization method may be any suitable method ranging from a simple logical iteration that ensures the stability of a well to a more sophisticated approach depending on the parameter being optimized and the fields located in the area. Here, area refers to a well environment, as depicting in FIGS. 1 and 2, comprising one or more wells. The parameter that is being optimized is the well contribution over the total gas produced at the gas processing facility/plant. For instance, during peak summer months, ethane is needed to generate more energy; thus, the ethane contribution per wells would be the parameter being optimized at that time. In one embodiment, referring to FIG. 1, the optimization method is selected by a user (i.e., user selections (122) via a user interface (124) of the user device (106). Alternatively, the optimization may be selected automatically based on a set of predetermined criteria.

Next, constraints are set for the system described here. The constraints may be manually input by a user, as described above, or automatically set based on a set of predetermined criteria. Non-limiting examples of constraints include maximum allowable gas rate, minimum pressure required for each well, and maximum water-gas ratio. In a next step, mole percentages of parameters of interest total of original blend (current production or original targets) are determined. This is also referred to as the weighted average total or blend total parameter. The goal is to identify the effect of each parameter of interest, or byproduct, at an individual well level. The calculation is performed as follows:

$\begin{matrix} Blend Total Parameter = Σ \frac{Well level parameter value \times Well Gas Rate}{Total Gas Rate} . & (1) \end{matrix}$

The following is an example of calculation of the blend total parameter in a three well system having the following parameters.

Well
Gas Rate (Cubic Feet per Day * 1000)
H₂S (%)

A
500
1%

B
200
1.5%

C
300
0%

Total Gas Rate=500+200+300=1,000 MSCFD (thousand standard cubic feet per day)

$Blend Total Parameter (H_{2} S) : \frac{1 % * 500 + 1.5 % * 200 + 300 * 0 %}{1000} = 0.8 % H_{2} S$

Non-limiting examples of parameters of interest include CO₂, H₂S, and chloride.

Subsequently, a correction factor is calculated for each well as follows:

$\begin{matrix} correction factor = \frac{Current Parameter Total (actual)}{Calculatded Parameter Total (estimated)} . & (2) \end{matrix}$

Thus, assuming an actual H₂S at the gas processing facility is 1%, the correction factor would be determined as follows:

$= \frac{1 %}{0.8 %} = 1.25 .$

The correction factor is applied immediately after calculation of the blend total parameter in equation (1) to adjust for the well level percentage of parameters, or byproducts. The correction is needed since the well level mole percentage is taken at different operating conditions (e.g., pressure and temperature) than what is operated at the gas processing plant level, which is the gathering point for the produced gas. The actual current parameter total value in equation (2) is the actual reading of the value being optimized at the gas plant for the entire gas blend (total gas production), whereas the estimated calculated parameter total in equation (2) is the calculated value from the well-by-well level as determined in equation (1).

Following the adjustment based on the determined correction factors, the system described herein sorts wells in a descending order list based on their contribution to the selected parameter's total. A wells count is identified and the first well in the list is selected. The system checks if the selected well meets the constraints criteria. If it does not, the next well in the list is selected. If the well does meet the constraints criteria, a rate is adjusted by increasing it some determined unit (e.g., rate=rate+1). The rate that is being adjusted is the raw gas rate of a single well that is measured in standard cubic feet of gas (thousands or millions). The rate adjustment is not limited to a specific value or unit, and 1 is used throughout this disclosure for illustration purposes only. The wells are then sorted in an ascending order based on their contribution to the parameter's total. The first well in that list is selected, and the system determines whether the selected well meets the constraints criteria. If not, the next well is selected from the list. If the well selected does meet the constraints criteria, then the rate is adjusted by decreasing it by the same value/unit as it was increased by previously (e.g., rate=rate−1).

Next, the new parameter total is calculated. The new parameter total is the well level total after adjustment (i.e., increase or decrease in raw gas rate). If the target is not achieved, the system selects the next top well contributor and proceeds to checking if the selected well meets the constraints criteria and continues the process from there. The “target” is the targeted amount of additional sales gas that is being demanded or the amount of gas that is required for optimization. The gas blend may be an addition to the originally allocated gas for each day, or a portion of the original gas that need to be optimized. In the optimization system/workflow depicted in FIG. 4, the target is achieved if the generated list of wells after optimization reaches the desired rate the user has requested for optimization. Operationally, a venturi reading at each well may be analyzed and/or the total gas production at the gas plant may be monitored.

FIG. 4 is a flow diagram illustrating the method for gas blend optimization described above. At a start of the algorithm, a target is assigned, or set, by a user or automatically (Block 400). A non-limiting example of an assigned target to be optimized is an additional gas rate that is required to be produced to meet a certain limitation (operationally or economically) or scenario. Next, an optimization method is selected (Block 402) and criteria for constraints are set (Block 404), as described above. The system according to embodiments of the present disclosure then calculates the weighted average total (Block 406) (equation 1). Subsequently, the correction factor for each well is determined (Block 408). The correction factor is determined based on a current real-world (i.e., actual) value (e.g., current parameter total) divided by a calculated value (i.e., calculated parameter total determined in Block 406), as in equation (2) above. The correction factor value is applied to the weighted average total calculated for each well in Block 406. Based on the adjusted values, the system described herein sorts wells (Block 410) in descending order (or ascending order) according to each well's contribution to the weighted average total. The ordering, or sorting, of wells is done to prioritize the selection of wells based on the available well level data to increase the performance speed of the algorithm.

A well count is then identified and the top contributing well in the list (i=1) is selected (Block 412). Once selected, the system according to embodiments of the present disclosure determines whether the top well (i=1) meets the constraints criteria (Block 414). If not, the system moves on to the next well in the list (i=i+1) (Block 416). If it does meet the criteria, the rate (i.e., the rate of raw gas production per well) is adjusted (Block 418). In one embodiment, the rate is decreased by one unit (i.e., rate=rate−1). Following the rate adjustment (Block 418), the system described herein then sorts wells in ascending order (or descending if ascending order was used first) according to each well's contribution to the weighted average total (Block 420). In Block 422, the lowest contributing well in the list is identified. Once identified, the system according to embodiments of the present disclosure determines whether the lowest contributing well (i=1) meets the constraints criteria (Block 424). If not, the system moves on to the next well in the list (i=i+1) (Block 426). If it does meet the criteria, then the rate is adjusted (Block 428). In one embodiment, the rate is increased by one unit/value (i.e., rate=rate+1). The order of increasing or decreasing the unit/value as adjustment is not critical. In other words, the rate may be increased first, then decreased, or decreased first, then increased. Significantly, it is critical to adjust the gas blend in both directions (increase and decrease) a certain volume to be consistent. For example, the rate of a Well A may be increased if it is determined that it helps in achieving the desired optimization outcome. Since the total gas rate needs to remain fixed, a Well B is identified in another sorting/listing to be reduced by the same amount as the increase in A, as its current rate does not help in meeting the desired optimization outcome.

Using the new rates, the system described herein calculates a new weighted average total for the wells (Block 430). The system then determines if the target has been met (Block 432). If not, the method returns to the first sorting of wells by contribution percentage (Block 410). If the target has been met, then the algorithm stops and a new optimized list is exported (Block 434). The new optimized list includes the list of wells that may be utilized for adjusting the gas blend to the specifications needed. Once generated, the list may then be shared with field operators who may ensure each well meets its new well rate. The generated list may be shared via a multitude of methods, including, but not limited to, an electronic transmission via text message and/or email, or a visual notification to a display utilized by field operators.

FIG. 5 illustrates a summarized output of an optimized gas blend in million standard cubic feet per day (MMSCFD), which may be generated by the system described herein. MMSCFD is a unit of measurement for gases which is commonly used as a measure of natural gas, liquified petroleum gas, compressed natural gas, and other gases that are extracted from a plant processing facility. The summarized output is an analytical tool that provides a summary of results following implementation of the optimization described herein. As shown in FIG. 5, the summarized output depicts well areas (areas 1-4) that were optimized along the x-axis and provides a summary of the rate change in gas production along the y-axis. The filled regions of the bars represent an amount (i.e., volume) of gas being increased for a single area/field, while unfilled regions represent an amount of gas being reduced to achieve the new optimized target (e.g., maximize condensate production, minimize CO₂emission).

The total amount of gas production is optimized by either increasing or decreasing the rates of sub-wells (corresponding to a list of wells for optimizing) that are located in each area. Referring to FIG. 5, depending on the area and optimization method, an area may experience only a decrease in total rate (e.g., area 1 (500)) and area 4 (502)), only an increase in total rate (e.g., area 3 (504)), or a decrease in rate for one sub-group of wells and an increase in rate for another list (or sub-group) of wells (e.g., area 2 (506)). The system according to embodiments of this disclosure identifies the sub-group of wells that need to be adjusted to follow the desired change in the overall blend, as illustrated in FIG. 4, and the rate change for those areas (sub-groups) of wells is directly influenced by the optimization.

In one or more embodiments, a comparison may be generated of previous (or current) values for a parameter being optimized in a sales gas versus the new values following the optimization process described herein. In its raw state, natural gas cannot be sold and, therefore, must be processed to meet certain specifications for sales gas. Sales gas is processed to remove liquified petroleum gas, condensate, and CO₂. Sales gas typically consists mainly of methane and ethane. Non-limiting examples of specifications for sales gas include a maximum hydrocarbon dewpoint temperature at a pressure of 800 pound-force per square inch in relation to atmospheric pressure (psig), a maximum allowable CO₂content, a maximum allowable H₂S content and total organic sulfur content, a maximum allowable water-vapor content, a maximum allowable temperature of gas leaving the gas processing plant, a minimum pressure to enter a gas transmission grid, and a minimum heating value. Each of these specifications may be used as parameters for optimization using the present invention. For example, the percent CO₂in an original sales gas may be compared with the percent CO₂in an adjusted sales gas using the system and method according to embodiments of the present disclosure. A visual output (or result), such as a graph or table, may be generated to allow a user to assess and quantify the change in the parameter being optimized (e.g., CO₂). Additionally, this type of summary provides a way to ensure that the additional gas is being optimized as intended.

Additionally, in one or more embodiments, the system described herein is configured to predict the overall blend outcome at the plant level. The gas optimization method described is used to optimize gas byproducts, such as CO₂, H₂S, N₂₊, from the source (i.e., untreated gas production) by identifying the effect of each byproduct at an individual well level. From that information, the gas optimization system determines which wells to utilize for adjusting the gas blend, and a future estimation is generated prior to implementation. Future estimation is used to determine the effect of adjusting the blend prior to actual implementation. The prediction of the parameter of interest is determined by the weighted average method of equation (1) described above. The selection of wells is controlled by a set of well level constraints (e.g., maximum surface pressure, maximum gas rate) to maintain the well health conditions.

One or more embodiments of the invention described herein may be implemented on a computing system. Any combination of mobile, desktop, server, router, switch, embedded device, or other types of hardware may be used. FIG. 6 illustrates an exemplary computing system (600). The computing system (600) may be one or more mobile devices (e.g., laptop computer, smart phone, personal digital assistant, tablet computer, or other mobile device), desktop computers, servers, blades in a server chassis, or any other type of computing device or devices that includes at least the minimum processing power, memory, and input and output device(s) to perform one or more embodiments disclosed herein. For example, as shown in FIG. 6 the computing system (600) may include one or more computer processor(s) (602), associated memory (604) (e.g., random access memory (RAM), cache memory, flash memory), one or more storage device(s) (606) (e.g., a hard disk, an optical drive such as a compact disk (CD) drive or digital versatile disk (DVD) drive, a flash memory stick), and numerous other elements and functionalities. The computer processor(s) (602) may be an integrated circuit for processing instructions. For example, the computer processor(s) may be one or more cores, or micro-cores of a processor.

The computing system (600) may also include one or more input device(s) (608), such as a camera, imager, touchscreen, keyboard, mouse, microphone, touchpad, electronic pen, or any other type of input device. Further, the computing system (600) may include one or more output device(s) (610), such as a screen (e.g., a liquid crystal display (LCD), a plasma display, touchscreen, cathode ray tube (CRT) monitor, or other display device), a printer, external storage, or any other output device. One or more of the output device(s) may be the same or different from the input device(s). The computing system (600) may be connected to a network (612) (e.g., a local area network (LAN), a wide area network (WAN) such as the Internet, mobile network, or any other type of network) via a network interface connection (not shown). The input and output device(s) may be locally or remotely (e.g., via the network (612)) connected to the computer processor(s) (602), memory (604), and storage device(s) (606). Many different types of computing systems exist, and the aforementioned input and output device(s) (608), (610) may take other forms.

Further, one or more elements of the computing system (600) may be located at a remote location and be connected to the other elements over a network (612). Further, one or more embodiments may be implemented on a distributed system having a plurality of nodes, where each portion of the embodiment may be located on a different node within the distributed system. In one embodiment, the node corresponds to a distinct computing device. In other embodiments, the node may correspond to a computer processor with associated physical memory. In yet other embodiments, the node may correspond to a computer processor or micro-core of a computer processor with shared memory and/or resources.

Software instructions in the form of computer readable program code to perform embodiments of the disclosure may be stored, in whole or in part, temporarily or permanently, on a non-transitory computer readable medium such as a CD, DVD, storage device, a diskette, a tape, flash memory, physical memory, or any other computer readable storage medium. Specifically, the software instructions may correspond to computer readable program code that, when executed by a processor(s), is configured to perform one or more embodiments of the disclosure.

The gas optimization system and method described herein has several advantages, such as the ability to optimize a gas blend instantaneously (i.e., within seconds). In contrast, existing technologies are very time consuming. Additionally, since less interaction is required by a user compared to existing technologies, there is an increase in productivity, an increase in accuracy, and less human error in optimizing the gas blend. Furthermore, the invention according to embodiments of the present disclosure may ensure compliance of several objectives in one run, such as operational limitations and sales gas specifications. For instance, while one parameter may be the focus of the optimization, the constraints set by a user prior to the optimization ensure the operational limitations are met. Non-limiting examples of operational limitations include ensuring the selection of only stable wells which do not shut down unintentionally and minimizing water production from wells to avoid future issues that may negatively impact operation. Additionally, unlike existing methods which have limited optimization for maximizing only production, the gas optimization system and method described herein may be utilized to enhance the overall quality of the produced gas.

Although only a few example embodiments have been described in detail above, those skilled in the art will readily appreciate that many modifications are possible in the example embodiments without materially departing from this invention. Accordingly, all such modifications are intended to be included within the scope of this disclosure as defined in the following claims. In the claims, means-plus-function clauses are intended to cover the structures described herein as performing the recited function and not only structural equivalents, but also equivalent structures. Thus, although a nail and a screw may not be structural equivalents in that a nail employs a cylindrical surface to secure wooden parts together, whereas a screw employs a helical surface, in the environment of fastening wooden parts, a nail and a screw may be equivalent structures. It is the express intention of the applicant not to invoke 35 U.S.C. § 112(f) for any limitations of any of the claims herein, except for those in which the claim expressly uses the words ‘means for’ together with an associated function.