HIGH PRESSURE GAS LIFT OPTIMIZATION

Abstract

An automated HPGL optimization system and method for enhancing or optimizing an injection rate of a HPGL gas injection system. A controller adjusts one or more compressor parameters (e.g., compressor speed and/or injection rate) in relation to the bottom hole pressed based on the surface injection pressure of the well. The system and method identifies, adjusts and maintains gas injection rates resulting in optimized injection rates and/or pressures. The optimized production injection rate or pressure, which is related to bottom hole pressure, is determined by proxy using a pressure sensor located at the surface for measuring the discharge pressure of the compressor. Compressor parameters are periodically adjusted identify a minimum bottom hole flowing pressure based on the surface injection pressure of the well. The compressor is then adjusted to a set point associated with the minimum bottom hole pressure.

Description

CROSS REFERENCE

This application claims the benefit of the filing date of U.S. Provisional Patent Application No. 63/749,233 having a filing date of Jan. 24, 2025, the entire contents of which is incorporated herein by reference.

FIELD

The subject matter of the present disclosure refers generally to high pressure gas lift systems and methods for enhancing hydrocarbon recovery from an oil and gas well.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a schematic diagram of a compressor skid unit, in an embodiment.

FIG. 2 shows a perspective view of a compressor skid unit, in an embodiment.

FIG. 3 shows a side elevational view of a compressor skid, unit in an embodiment.

FIG. 4 sows a configuration of an exemplary High-Pressure Gas Lift (HPGL) set up, in an embodiment.

FIG. 5 shows a HPGL downhole schematic, in an embodiment.

FIG. 6A shows a bottom hole pressure graph relative to a gas injection rate, in an embodiment.

FIG. 6B shows an oil production rate relative to a gas injection rate, in an embodiment.

FIG. 7A shows an oil production rate relative to a gas injection rate for tubing flow, in an embodiment.

FIG. 7B shows an oil production rate relative to an optimized gas injection rate for tubing flow, in an embodiment.

FIG. 8A shows an oil production rate relative to a gas injection rate for annular flow, in an embodiment.

FIG. 8B shows an oil production rate relative to an optimized gas injection rate for annular flow, in an embodiment.

FIG. 9A shows daily profits relative to reservoir pressure, in an embodiment.

FIG. 9B shows incremental daily profits relative to reservoir pressure, in an embodiment.

FIG. 10 shows a gas injection optimization process, in an embodiment.

FIG. 11 shows a plot of three average injection pressures fit to a parabolic curve, in an embodiment.

FIG. 12 shows a plot of three average injection pressures fit to a parabolic curve, in an embodiment.

FIG. 13 illustrates graphs of injection pressures over time and a compressor speed over time for annular flow, in an embodiment.

FIG. 14 illustrates graphs of injection pressures over time and a compressor speed over time for annular flow transitioning to tubing flow, in an embodiment.

FIG. 15 illustrates graphs of bottom hole pressure and conversion to a lift gas utilization factor, in an embodiment.

DETAILED DESCRIPTION

Wellbores drilled for the production of oil and gas often produce fluids in both the gas and liquid phases. Produced liquid phase fluids may include hydrocarbon oils, natural gas condensate, and water. When a well is first completed, the initial formation pressure is typically sufficient to force liquids up the wellbore and to the surface along with the produced gas. However, during the life of a well, the natural formation pressure tends to decrease as fluids are removed from the formation. As this downhole pressure decreases over time, the velocity of gases moving upward through the wellbore also decreases, thereby resulting in a steep production decline of liquid phase fluids from the well. Additionally, the hydrostatic head of fluids in the wellbore may significantly impede the flow of gas phase fluids into the wellbore from the formation, further reducing production. The result is that a well may lose its ability to naturally produce fluids in commercially viable quantities over the course of the life of the well.

In order to increase production from such a well, various artificial lift methods have been developed. A common and well-established artificial lift method is gas lift. In gas lift methods, a gas is injected into the wellbore downhole to lighten, or reduce the density of, the fluid column by introducing gas bubbles into the column. A lighter fluid column results in a lower bottom hole pressure, which increases fluid production rates from the well. Gas lift is a method that is very tolerant of particulate-laden fluids and is also effective on higher gas oil ratio (GOR) wells. As such, gas lift has become a commonly utilized artificial lift method in shale oil and gas wells.

In conventional gas lift methods, a gas lift compressor at the surface injects gas through multiple gas lift valves positioned vertically along the production tubing string. Conventional gas lift compressors typically have a discharge pressure in the range of 1,000 to 1,200 psig. However, there are disadvantages in conventional gas lift compressor systems. For instance, the fluid lift rates achievable by conventional gas lift compressors are typically limited, which limits the effectiveness of gas lift operations. Although conventional gas lift compressors may achieve higher lift rates than some other artificial lift methods, such as beam pumping, gas lift typically does not produce the same lift rates as other methods such as electric submersible pumps (ESPs).

To overcome limited fluid lift rates, the use of high-pressure gas lift (HPGL) compressors has gained traction in the oil and gas industry in recent years, and the use of HPGL booster compressors has increased rapidly since 2017. The HPGL process is a variation on conventional gas lift methods in which no gas lift valves are required in the production tubing string. Instead, compressed gas is injected into the wellbore fluid column near the end of tubing (EOT), thereby reducing the density of the entire fluid column, which provides higher production rates as compared to conventional gas lift methods. Like conventional gas lift, HPGL is tolerant of particulate-laden fluids and high GORs and typically provide fluid lift rates comparable to ESPs. However, the HPGL process requires a source of compressed gas at a significantly higher pressure than the compressed gas utilized in conventional gas lift processes. HPGL gas lift compressors are typically designed to produce compressed gas at a discharge pressure of up to 4,000 psig or more to provide an adequate injection gas flow rate.

FIG. 1-3 illustrate a natural gas compressor package, in an embodiment. In this illustrated embodiment, the natural gas compressor package is a self-contained and skid mounted system allowing the system to be portable. In such an embodiment, the optimization control system disclosed herein may be an entirely on-skid system requiring no off-skid infrastructure. Though discussed in relation to an on-skid embodiment utilized for high pressure gas injection into wells, it will be expressly understood that aspects of the present disclosure may be utilized with off-skid systems as well as other compressor systems. The on-skid gas injection system is provided by way of example and not by way of limitation. FIG. 1 shows a schematic diagram of a compressor skid, in an embodiment. FIGS. 2 and 3 illustrate the exemplary compressor skid in greater detail. In each of these figures, the compressor system includes an illustrative two-throw reciprocating compressor, which may be used to provide gas lift to two wells. However, this is not a requirement.

As shown in FIG. 1, the natural gas compressor package or “compressor system” 10 includes a compressor 14 having a plurality of compressor cylinders 16. A compressor engine 18 is operably coupled to the compressor 14 and configured to simultaneously drive each of the compressor cylinders 16. Thus, the system may utilize a single compressor engine 18 (e.g., natural gas engine) to operate all of the compressor cylinders 16. As used herein, a “compressor cylinder” refers to a cylinder having a piston disposed therein to compress and displace gas within the cylinder, wherein the piston is driven by a rotating crankshaft coupled to the compressor engine 18. Thus, the compressor engine 18 is operably coupled to each of the compressor cylinders 16 and configured to simultaneously drive each of the compressor cylinders 16 by driving the crankshaft disposed in a crankcase, which drives each piston contained within each cylinder in the plurality of compressor cylinders 16. Each compressor cylinder 16 has a gas inlet line 42 and its own dedicated gas outlet line 44, which may supply compressed gas to, for example, a well. The gas in the inlet gas line 42 may be compressed in the first stage and then pass through a cooler 34 before being further compressed to its final discharge pressure in the second stage. In an exemplary embodiment, a single compressor skid 20 may provide wellbore injection gas to one or more individual wells, however this is not a requirement.

To independently control the gas flow rate to each well, the compressor system 10 may further comprise one or more control valves 40 each corresponding to a respective compressor cylinder 16. Each control valve 40 may be positioned on a gas inlet line 42 upstream of a compressor cylinder 16, as seen in FIG. 1. Each control valve 40 is configured to independently control the suction pressure to each respective compressor cylinder 16 and thereby to independently control a gas flow rate through the gas outlet line 44 of each compressor cylinder 16. These control valves 40 are, in an embodiment, pneumatically controlled by a flow controller 48, which opens and closes the valves using compressed natural gas. Thus, the valves/controller are pneumatic devices. To control the gas flow rate through each of the gas outlet lines 44, the compressor system 10 may include flow meters 46 and/or the flow controllers 48 each corresponding to one of the control valves 40.

The compressor skid 20 shown in FIGS. 1-3 illustrate a common cooler structure 30 may be utilized to cool compressed gas streams from all of the separate compressor cylinders 16, as well as to provide cooling water to the compressor engine 18. The cooling structure 30 may include separate cooling sections 32 designated for the compressed gas process streams. The skid 20 may also include a compressor exhaust 38 for the compressor engine 18. The cooler 30 may define a radiator. A pneumatic actuator may control air flow through the cooler (e.g., by controlling louvers of an air flow path of the cooler; not shown).

In an embodiment, the compressor skid 20 comprises a plurality of scrubbers 28 each corresponding to a respective compressor cylinder 16. The scrubbers 28 are configured to remove liquid droplets, which may include a variety of liquid hydrocarbons that may condense out of the gas stream.

As shown in FIGS. 2 and 3, components of the compressor system 10 share a common skid unit frame 22 to which the components may be mounted to provide a portable skid-mounted compressor unit 20 that can be transported to any field location. As used herein, a “skid” refers to a compressor system having components mounted onto a frame 22 so that the system may be transported as a single unit 20. In addition, the skid is sized so that the unit may be transported by cargo truck or rail as a single unit to any location as needed. However, aspects of the disclosure may be implemented in non-skid (e.g., fixed) compressor systems. The compressor system 10 may have one or more gas inlet line flanges 35 and one or more gas outlet line flanges 36 for connecting the gas inlet lines 42 and the gas outlet lines 44. In addition, a control panel 26 may be included with the compressor system 10. The control panel 26 may include a programmable logic controller (PLC), not shown and/or a computer server, not shown, suitable for controlling operations of compressor system (e.g., compressor speed and/or capacity; hereafter speed/capacity) and managing calculations and control functions necessary to carry out the methods disclosed herein. The computer server may be located at the wellsite or may be remotely located and accessed as a cloud server or other remote server. In an embodiment, the computer server will perform the necessary calculations and control the operations of the PLC. However, any computer arrangement (e.g., on-site or remote) may be used to perform the operations necessary for carrying out the disclosed methods.

FIG. 4 illustrates one non-limiting embodiment of a schematic representation of a high-pressure gas lift (HPGL) setup at a wellhead 100. As illustrated, a primary compressor and a booster compressor are utilized to inject high pressure gas (e.g., 4000 psig) into the wellhead 100. In one embodiment, the compressed gas may be injected into production tubing 110 disposed within well casing 120 such that the well is produced through the annulus between the production tubing and the well casing 120 (annular flow). In another embodiment, the compressed gas may be injected into the annulus between the well casing 120 and the production tubing 110 such that the well is produced through the production tubing 110 (e.g., tubing flow). Various valves may be set to selectively direct the compressed gas into the wellhead 100. One or more pressure sensors 106, 108 may be attached to the well casing 120 or production tubing 108 to monitor the injection pressure (e.g., compressor discharge pressure). However, it will be appreciated that pressure sensors may be located at other locations for monitoring such discharge pressures. Though not illustrated in FIG. 4, it will be appreciated that a controller (e.g., control panel 26 and/or server) may be connected to the various compressors, pressure sensors and valves to automate their control. FIG. 5 illustrates a lower end of the well where high pressure injected gas is injected through the production tubing 110 and exits into the well casing 120 and into the wellbore fluid column near the end of tubing (EOT).

The present disclosure focuses on enhancing oil production in unconventional wells through the optimization of gas injection rates in HPGL. By conducting software simulations and, in some embodiments, incorporating economic analysis with variable oil prices and gas injection costs, potential benefits of HPGL optimization are set forth during the initial decline phase of unconventional wells. Also compared are the outcomes of employing optimized HPGL injection rates against a fixed injection rate (e.g., 1.2 million standard cubic feet per day; hereafter MMSCF/D), which reflects the design gas injection rate (e.g., speed/capacity) for a commercially available HPGL booster compressor. One non-limiting objective was to highlight the advantages of HPGL optimization over static methods to improve the management of unconventional oil well production and artificial lift systems.

One important aspect of gas lift optimization is knowing the bottom hole pressure during gas injection. Traditional methods typically rely on multiphase fluid flow correlations to estimate this pressure from estimated oil, water, and gas production rate measurements. Additionally, production rates in unconventional wells decline rapidly in the first months of production, making the inflow performance relationship (IPR) difficult to characterize. The optimal or more optimal injection rate is therefore a rapidly moving target rendering manual adjustment of gas injection rate by field personnel inefficient.

As disclosed, the single point injection configuration of HPGL is exploited to more precisely estimate bottom hole pressure compared to multiphase fluid flow correlations. This technique allows for a more precise estimation of bottom hole flowing pressure based on surface gas injection pressure measurements, eliminating the need for complex and costly downhole measurement equipment. This approach improves the reliability of bottomhole pressure measurements as compared to multiphase flow correlations, enabling the optimization of oil well production operations.

In HPGL operations, precise monitoring and adjustment of operating parameters based on the bottom hole pressure (BHP) is beneficial. Typically, this is driven by the objective of maximizing oil production, which is directly influenced by the magnitude of drawdown, which is the difference between reservoir pressure and the bottomhole pressure. A larger drawdown increases oil output, as it facilitates a greater flow of oil from the reservoir to the wellbore. Therefore, continuous, accurate monitoring of BHP provides a basis for efficient operation of HPGL systems. That is, operating parameters of HPGL may be adjusted to minimize the bottom hole pressure, increase the magnitude of drawdown and maximize oil production.

FIG. 6A illustrates the bottom hole pressure versus gas injection rate. As illustrated, as the gas injection rate into the well is increased, gas dilutes the oil column in the well, causing its hydrostatic pressure to decrease. This reduction in pressure results in a corresponding decrease in BHP. This trend continues until a specific point is reached where the BHP is at its lowest. At this point, the gas injection rate is considered optimal, aligning with the maximum oil production potential as illustrated in FIG. 6B. However, increasing the gas injection rate beyond this optimum point reverses this trend. Increased frictional losses due to excessive gas injection elevate the BHP and adversely impact oil production. The negative impact of over-injection on production efficiency is clearly demonstrated in FIGS. 6A and 6b, highlighting the importance of setting the gas injection rate within optimal limits in HPGL operations to ensure peak oil production.

The following formula (Eq.1) describes the relationship between the surface pressure of gas injection (P_s) and the bottom hole well flowing pressure (P_wf) which is also referred to as bottom hole pressure BHP, accounting for a wellbore angle (θ). It considers the friction losses, with a friction factor (f_f), the specific gravity of the gas (γ_g), and the average gas compressibility factor (Z_avg). Additionally, the formula considers the diameter of the tubing string (D) through which gas is injected, the rate of this injection (q), the average temperature (T_avg) within the system, and the vertical depth (H) of the well.

$\begin{array}{cc}{P}_{\mathrm{wf}}^2=P_s^2*e^{-s}+2.685*10^{-3}*\frac{f_f*{\left(Z_{\mathrm{avg}}*T_{\mathrm{avg}}*q\right)}^2}{\sin \left(\Theta \right)*D^5}*\left(e^{-s}-1\right)& \left(1\right)\end{array}$ $\mathrm{Where}$ $\begin{array}{cc}s=\frac{-0.0375*{\gamma }_g\sin \left(\Theta \right)*H}{Z_{\mathrm{avg}}*T_{\mathrm{avg}}}& \left(2\right)\end{array}$

However, for a vertical well and neglecting friction, the bottom hole flowing pressure (Pwf) can be calculated from the surface gas injection pressure (Ps) using a much simpler formula as described below in Eq. 3

$\begin{array}{cc}{P}_{\mathrm{wf}}=P_s{e}^{0.01875\frac{\gamma H}{Z_{\mathrm{avg}}*T_{\mathrm{avg}}}}& \left(3\right)\end{array}$

If the injection depth (H), gas specific gravity (γ_g), average compressibility factor (Z_avg), and average temperature (T_avg) remain constant, the surface injection pressure is directly proportional to the bottom hole pressure. Estimating bottom hole pressure from the gas column in single point injection is typically more reliable than using a multiphase fluid correlation, which relies on well tests and other flow parameters that are often uncertain. Validation of this method requires proving the insignificant the friction losses and the effects of temperature and gas specific gravity change on the bottom hole pressure. It is known that increasing the gas injection rate leads to higher friction losses. However, when the injection pressure is increased, the friction losses significantly drop. It has been determined that at injection pressures above 1000 psi with a gas rate of 2 MMSCF/D, the friction loss is less than approximately 5 psi. This finding is particularly relevant for high pressure gas lifting operations, which operate at high gas injection pressures. In such scenarios, the friction losses due to gas injection are minimal.

Likewise, the impact of temperature and specific gravity changes on the bottom hole pressure (BHP) have been determined to be minimal. For instance, it has been determined that even with an average temperature change of 50° F., the resultant change in BHP is less than 2%. This minimal deviation is also mirrored in scenarios involving alterations in gas specific gravity. Modifications in specific gravity yield a similarly negligible effect on BP. For instance, with a change in the gas specific gravity of 0.05, the bottomhole pressure change is about 1.5%. These findings show the relative insensitivity of the BHP estimation method to fluctuations in temperature and gas specific gravity.

Considering the minimal influence of these factors, the formula for BHP can be further simplified resulting in Eq. 4. This equation provides a simple and direct approach for efficient bottomhole pressure (P_wf) monitoring during HPGL as estimated from the surface gas injection pressure (P_s), which is readily measurable.

$\begin{array}{cc}{P}_{\mathrm{wf}}=P_s*K& \left(4\right)\end{array}$ $\begin{array}{cc}K=e^{\frac{0.01875*{\gamma }_{g*H}}{Z_{\mathrm{avg}}*T_{\mathrm{avg}}}}& \left(5\right)\end{array}$

If the depth of injection H, gas specific gravity γ, average compressibility factor Z, and average temperature T are constant or nearly so, as assumed, then the surface injection pressure serves as a proxy for bottomhole pressure without the need for an expensive and/or unreliable downhole pressure sensor. Eq. 4 therefore suggests a straightforward approach: increase oil production by keeping the wellhead gas injection pressure as low as possible to maintain the bottom hole pressure at a minimum. However, the inventors of the present disclosure have determined that it may not always be optimal to maintain the bottom hole pressure at a minimum to maximize oil production. That is, the inventors have determined, when including economic analysis such as oil prices, costs involved with injecting gas and/or the lost opportunity costs of injecting increased amounts of gas into a single well, that injection of HPGL gases at rates and/or pressures that maximizes oil production may not maximizes profits and may limit the availability of injection gas use that may be better used for injection in other wells.

In an embodiment, computer simulations were employed to simulate oil production rates in unconventional horizontal oil wells located in an exemplary oil producing basin (e.g., Permian basin). The simulations were conducted to explore the effect of varying HPGL gas injection rates on oil production rates as the reservoir pressure declines with time. Using Hagedorn and Brown multiphase fluid flow correlations, which are well known to those skilled in the art, the simulations were conducted under specific assumptions regarding reservoir characteristics, fluid properties, tubular dimensions, and economic factors. A reservoir pressure of 6,000 psi and a tubing head pressure set at 200 psi was assumed. Regarding fluid properties, the water cut was established at 60%, the Gas Oil Ratio (GOR) at 1,300, the oil gravity at 40 API, and the gas gravity at 0.7. The tubular specifications included a tubing size of 2⅜ inches and a casing size of 5½ inches in an oil well with a TVD of 10000 ft. Economically, the model incorporated an oil price of $80 per barrel and a gas injection cost of $1 per thousand standard cubic feet per day (MSCFD). These parameters provided a comprehensive framework for HPGL simulation. One embodiment of Hagedorn and Brown multiphase fluid flow correlations is set forth in co-owned U.S. Pat. No. 11,572,771, the entire contents of which is incorporated herein by reference.

To simulate a high production rate well in the exemplary basin, a productivity index of 3 was used to construct a Vogel Inflow Performance Relationship or Vogel IPR curve. For tubing flow at a reservoir pressure of 6,000 psi, FIG. 7A shows that as the gas injection rate increases, the oil production rate increases. This continues until it reaches a peak where the maximum oil production rate of 1,621 stock tank barrels per day (STB/D) happens at a gas injection rate of about 1.2 MMSCF/D. But, if considering the cost of gas injection and oil price and replacing the oil production with daily profiles as shown in FIG. 7B, the optimal point of gas injection changes. Under these assumptions, the optimal gas injection rate for making the most profit is lower than 1.2 MMSCF/D and is actually closer to 0.8 MMSCF/D. That is, the reduced rate of injection lowers injection costs resulting in higher profits and frees additional gas (e.g., 0.4 MMSCF/D) for other uses. FIG. 7B identifies this observation: the optimal gas injection rate for maximizing profits is approximately 0.8 MMSCF/D, which yields a maximum daily profit of around $128,800, in the simulated example. This finding clearly demonstrates that the gas injection rate ideal for maximizing oil production does not necessarily coincide with the rate that maximizes profits. This distinction underscores the importance of incorporating economic factors into HPGL optimization process, in some embodiments.

Applying the same concept for annular flow at a reservoir pressure of 6,000, FIG. 8A shows that the oil production rate hits its maximum at 3,580 STB/D at a gas injection rate of 8 MMSCF/D. However, the scenario shifts when incorporating economic factors into the analysis. By considering the costs associated with gas injection and the fluctuating oil prices and putting the daily profits on the y-axis instead of oil production, a different conclusion is reached. Under these financial parameters, the most profitable gas injection rate is shown to be lower than 8 MMSCF/D. This adjustment in the optimal point describes the role of economic considerations in determining the most effective gas injection rate for maximizing profitability in oil production operations.

FIG. 8B reveals that the optimal gas injection rate for maximizing profits is around 6 MMSCF/D, yielding a maximum daily profit of approximately $280,000. In addition to costing less to inject 6 MMSCF/D rather than 8 MMSCF/D, an additional 2 MMSCF/D of injection gas remains for injection in other wells. While these results are exemplary and typically utilize higher overall gas injection rates than currently used in industry, these results clearly demonstrate that the most effective point for maximizing production does not necessarily coincide with the point for maximizing profits. Again, this emphasizes the importance of considering economic aspects, including the costs of gas injection, oil prices and lost opportunity costs, in determining the best approach for cost effective HPGL.

To show the potential benefits of optimizing HPGL, two different scenarios are considered. The first scenario represents a fixed gas injection rate of 1.2 MMSCF/D. The second scenario involves using the optimal gas injection rate computed from computer simulation, along with economic analysis that considers both oil prices and the cost of injecting gas.

FIG. 9A compares the daily profits that could be obtained when using the optimal gas injection rate against a fixed rate of 1.2 MMSCF/D as the reservoir pressure declines. The magnitude of the benefit is not immediately obvious. However, FIG. 9B, which shows the daily incremental profits achieved by using an optimized gas injection rate, illustrates that consistently using an optimized gas injection rate, which changes as the reservoir pressure declines, rather than using a fixed rate of, for example, 1.2 MMSCF/D, can lead to a substantial increase in profits. There is a potential for an incremental daily profit of around $11,000, in the exemplary embodiment, at the midpoint of the reservoir's life. This clearly demonstrates the substantial financial benefits that can be achieved through HPGL optimization.

Based on the foregoing discussion, the benefits of optimizing production of oil and gas wells in relation to the bottom hole pressure are clear with or without economic analysis. However, in practice, most wells are not optimized for a variety of reasons. For instance, a single operator may be responsible for operating numerous wells, possibly even several hundred wells. Accordingly, general practice has been to use a fixed injection rate (e.g., 1.2 MMSCF/D) across all wells in a basin or geographic area due to a lack of time to implement individual well optimization. In addition, the inability to reliably measure or estimate a bottom hole pressure in wells without downhole sensors has further limited the implementation of HPGL optimization. Accordingly, one aspect of the present disclosure is directed to an automated HPGL optimization system and method.

The exemplary compressor system 10 is particularly suited for carrying out the following methods for automatically optimizing HPGL injection rates and/or pressures thereby improving well production and/or well economics. Automated/dynamic control (e.g., by controller 26) of one or more compressor parameters (e.g., compressor speed and/or injection rate) may be implemented in relation to the bottom hole pressed based on the surface injection pressure of the well. The methods disclosed below provide the ability to identify, adjust and maintain gas injection rates which result in optimized injection rates and/or pressures. The optimized production injection rate or pressure, which is related to bottom hole pressure, is determined by proxy using a pressure sensor located at the surface for measuring the discharge pressure of the compressor or any other convenient location capable of monitoring pressure at the wellhead (production tubing pressure sensor, casing pressure sensor, etc.). Automated/dynamic control (e.g., by controller 26) of one or more compressor parameters is performed to periodically identify a minimum bottom hole flowing pressure based on the surface injection pressure of the well.

FIG. 10 illustrates one exemplary process 200 for automated HPGL optimization. As shown, the process 200 includes five steps. The first step is compressor startup 202. During compressor startup 202, the compressor is run at 50%, 75% and 100% speeds or compressor capacities (speed/capacity) for equal time periods (e.g., 24 hours) to build an initial library (e.g., data set) relating the different speeds/capacities of the compressor to the corresponding surface injection pressures, which are proxies for corresponding bottom hole pressures. A non-linear regression analysis is performed on the data set. A minimum value of the non-linear regression analysis is identified as the lowest average injection pressure corresponding to a minimum bottom hole pressure. Any non-linear regression analysis may be performed including, without limitation, fitting of a quadratic equation to the data set, fitting of a higher order polynomial to the data set and/or performing least squares analysis of the data set. In the following discussion, a quadratic equation is fit to the data set. However, it will be appreciated that the system and method are not limited to use of such a quadratic equation. In the second step, the compressor is run 204 for a predetermined run-time (e.g., one week, two weeks etc.) at a speed/capacity that corresponds to the lowest average injection pressure as determined from the start up step 202. In the third step 206 at the end of the run-time of step 204, the compressor speed/capacity is perturbed up and/or down (e.g., ±10% or ±20% compressor speed) to generate a new set of surface pressures and corresponding bottom hole pressures (new data set). In a fourth step, the new injections pressures and corresponding bottom hole pressures are analyzed 208 to determine a new lowest average injection pressure. Again, a non-linear regression analysis is performed on the new data set. A minimum value of the non-linear regression analysis is identified as the lowest average injection pressure corresponding to a minimum bottom hole pressure. In the fifth step, the compressor is set 210 to run at a speed/capacity that corresponds to the new lowest average injection pressure as determined from the perturbation step 206. This new lowest average injection pressure may be different than the prior average injection pressure accounting for changing conditions of the well (e.g., changes in reservoir pressure etc.). The compressor may again run for a predetermined run-time (e.g., one week, two weeks etc.) at which time steps 206, 208 and 210 may be repeated.

The following provides a more detailed description of the process 200 of FIG. 10.

202 Compressor startup:

• • a. Run compressor at 50%, 75%, and 100% speeds for predetermined time periods (e.g., 24 hours) and build a plot of identified discharge pressures (P_d) vs. compressor speed/capacity (e.g., percentage) (S) and determine initial compressor speed corresponding to minimum pressure using non-linear regression.
  • b. Fit a parabola to the data in the form of P_d=aS²+bS+c
  • c. The optimal speed/capacity corresponds to

$S=-\frac{b}{2a}$

• • d. If S≥100%, set at S=100%
  • e. If 0%<S<100%, adjust speed/capacity and other control variables to run at S.
  • f. If S≤0% turn the compressor off.
  • g. Leave compressor at the setpoint S for predetermined run-time, step 204.

Step 206 perturb the compressor system to generate a new optimization dataset.

• • a. If S=100%, perturb down 10% and observe discharge pressure at a predetermined time-period later (e.g., 24 hours).
    • i. If discharge pressure increases, go back to S=100% and leave for predetermined time-period (e.g., 2 weeks). Then repeat this step (Step 206).
    • ii. If discharge pressure decreases, further perturb down (e.g., try S=80%) and observe discharge pressure 24 hours later. Then go to Step 210.
  • b. If 0%<S<100%, perturb up and down 10% (with S_max and S_min limits of 100% and 0% respectively) and observed discharge pressure after at a predetermined time-period of stabilization (e.g., 24 hours). Go to step 210.

Step 210 analyze data. Determine new compressor speed using non-linear regression.

• • a. Fit a parabola to the data in the form of P_d=aS²+bS+c
  • b. The optimal speed/capacity corresponds to

$S=-\frac{b}{2a}$

• • c. If S≥100%, set at S=100%
  • d. If 0%<S<100%, adjust speed/capacity and other control variables to run at new S.
  • e. If S≤0% turn the compressor off.
  • f. Leave compressor at the setpoint S for predetermined run-time, step 206.

FIG. 11 illustrates a plot of three average injection pressures obtained in step 202 during startup of an exemplary well utilizing tubing flow. As illustrated, a first pressure point 212 is recorded against the injection rate that corresponds to 50% of the maximum compressor speed/capacity, a second pressure point 214 is recorded against the injection rate that corresponds to 75% of the maximum compressor speed/capacity, and a third pressure point 216 is recorded against the injection rate that corresponds to 100% of the maximum compressor speed/capacity. Solving the quadratic equation (e.g., parabolic equation) allows for identifying the vertex (e.g., minimum) of the parabola (i.e., S=−b/2a). This speed/capacity set point S or speed/capacity fraction corresponds to approximately 78%, in the illustrated embodiment. Accordingly, the compressor speed/capacity set to this set point S (e.g., updated or optimized set point) for a predetermined run-time (e.g., one week, two weeks etc.) after which the process is repeated un step 206. The example illustrated in FIG. 11 illustrates a situation when the set point S is between 0% and 100% of the maximum injection speed/capacity of the compressor allowing the compressor to be set to the identified optimized set point. It will be appreciated that the process for subsequent analysis during the perturbing step 206 is similar though the initial point is the set point S and the additional set points may be ±10% or ±20% compressor set point S.

FIG. 12 illustrates a plot of three average injection pressures obtained in step 202 during start-up of an exemplary well utilizing annular flow. As illustrated, a first pressure point 218 is recorded against the injection rate that corresponds to 50% of the maximum compressor speed/capacity, a second pressure point 220 is recorded against the injection rate that corresponds to 75% of the maximum compressor speed/capacity, and a third pressure point 222 is recorded against the injection rate that corresponds to 100% of the maximum compressor speed/capacity. Solving the quadratic equation (e.g., parabolic equation) for this example results in identifying a set point S of 1.557. As the identified set point is greater than 100% of the compressor speed/capacity (i.e., S≥100%) the compressor is set to 100%. In this example, running the compressor at maximum speed/capacity results in the best overall production moving the bottom hole pressure closer to its minimum value. That is, 100% compressor speed/capacity results in the minimum achievable discharge pressure and minimum achievable bottom hole pressure for the capacity of the compressor. While not achieving an actual minimum bottom hole pressure, running the compressor at 100% may still result in improved production compared to running the compressor at a fixed injection rate that is lower than 100% speed/capacity.

FIG. 13 illustrates plots of automated optimization for an annular flowing well over one year. As shown, the downward sloping line 224 of the upper graph represents the discharge pressure Pd of the compressor. As shown, the discharge pressure Pd declines over the course of the year due to the changing conditions of the well (e.g., reservoir pressure). The compressor speed line 226 of the lower graph illustrates periodic (e.g., every two weeks) perturbing the operating conditions of the compressor system to generate a dataset for use in determining an optimized speed or capacity set point S for the compressor. Initially, a compressor start-up analysis (e.g., step 202 of FIG. 10) is performed as illustrated by the near vertical line 228 on the left side of the compressor speed/capacity line 226. Based on the analysis of the data, the compressor is set at a 100% speed/capacity after analysis of the start-up data set. The periodic notches 230 in the compressor speed/capacity line 226 represent the period perturbing of the operating conditions of the compressor to generate subsequent data sets that are analyzed to determine a new compressor speed/capacity set point S. In this example of annular flow, the well is produced at 100% operating speed/capacity of the compressor for the entire year. In this example, the well never reaches an over-injection state. Along these lines, it has been found that in some instances, automated optimization of the compressor provides minimal benefits for annular flow wells though determining that the well should be injected at a 100% compressor speed/capacity may still provide benefits over a fixed injection rate (e.g., speed/capacity) that is less than 100%. Further, lift gas utilization based on economic factors may still be optimized as discussed herein.

FIG. 14 illustrates plots of automated optimization for a well that is initially annular flowing, and which switches to tubing flow at sixty days. As shown, the downward sloping line 232 of the upper graph represents the discharge pressure Pd of the compressor. The discharge pressure Pd declines over the initial sixty-day period 234 while the well is annular flowing. The discharge pressure Pd increases when switched to tubing flow as represented by vertical line 236 (e.g., due to smaller volume of the tubing relative to the well casing). After tubing flow is initiated at sixty days, the discharge pressure continues to decline over the course of the year due to the changing conditions of the well (e.g., reservoir pressure) as illustrated by sloping line 238. The compressor speed line 240 of the lower graph illustrates periodic (e.g., every two weeks) perturbing of the operating conditions of the compressor system to generate a dataset for use in determining an optimized speed or capacity set point S for the compressor. Initially, a compressor start-up analysis (e.g., step 202 of FIG. 10) is performed as illustrated by the near vertical line 242 on the left side of the compressor speed/capacity line 240. Based on the analysis of the data, the compressor is set at a 100% speed/capacity after analysis of the start-up data set. The operating conditions (e.g., speed/capacity) of the compressor are perturbed every two weeks (not shown) and the compressor set point remains at 100% through the transition between annular flow and tubing flow until day 216. At this point 244, a periodic perturbing of the compressor identifies an over injection situation, and the speed/capacity set point is adjusted to 86%. Two weeks later, a further over injection situation is identified at point 246 and the speed/capacity set point is adjusted to 80%. In the illustrated embodiment, the automated optimization identifies and corrects for over-injection in tubing flow cases. Analysis of the production of the well shows an incremental increase in the overall oil production of the well over the course of the year compared to running at 100% for the entire year. Further analysis shows significant increase in overall oil production compared to a fixed injection rate/compressor speed that is less than 100%.

In summary, automated optimization of injection rates (e.g., compressor speed/capacity) can, in some instances, provide significant incremental oil production compared to fixed injection rates and at least marginal incremental oil production to contact 100% injection rates. However, the optimization where injection rates are set below 100% provides some additional benefits. Specifically, such injection rates below 100% frees gas for use in other wells, lowers compressor energy costs by avoiding 100% speed when a lower compressor set point delivers equal or better production, reduces wear on the compressor which operates fewer hours at maximum speed and overall reduces mechanical load (e.g., pressure) on system components.

Economic Optimization

As noted above in relation to FIGS. 7A-8B, operating a well at speed or capacity set point (e.g., injection rate) to produce a minimum bottom hole pressure may not always be the most economical operation of the well. Specifically, it has been determined that, based on the costs of injection gas into a well and/or lost opportunity costs of injecting additional gas with little economic return and which could be injected into other wells, there may be instances when it is beneficial to operate wells at gas injection rates (e.g., compressor speeds/capacities) that do not minimize the bottom hole pressure of the well. As illustrated in FIGS. 7A and 7B and discussed above, there is sometime a direct cost to operating a tubing flow well at a minimum bottom hole pressure. Likewise, FIGS. 8A and 8B show minimal or negative economic returns, in some instances, for operating an annular flow well at the minimum bottom hole pressure (FIG. 8A at 8 MMSCF/D) rather than at a lower gas injection rate (FIG. 8B at 6 MMSCF/D). Further, as illustrated in FIG. 8B, the return of operating a 6 MMSCF is substantially identical to operating at 4 MMSCF/D. Accordingly, an optimal injection rate can vary significantly depending on if production or economics is the optimizing target. The presented methodology can easily be implemented for economic optimization with minimal additional information.

Essentially, the same process is implemented as set forth above except the process is applies to optimize a lift gas utilization factor F_gu which is defined as a ratio of barrels of oil produced divided by thousands of standard cubic feet of per day of gas injection (BOPD/MSCFD). Such analysis requires various inputs from the operator. Such inputs may be well specific or may be generalized for a region or basin. The required inputs are a well productivity index J and the desired lift gas utilization factor F_gu. Thes are defined as follows:

• • Well productivity index, J=f(P_wf, Q_o, t, . . . )
  • a. Simplest form, J=constant
  • b. Example: J=1 STB/D/psi
  • Desired lift gas utilization factor, F_gu
  • c. Example:

$F_{\mathrm{gu}}=0.3\frac{\mathrm{STB}}{\mathrm{MSCF}}\mathrm{or}{F}_{\mathrm{gu}}=0.3\frac{\mathrm{STB}\mathrm{oil}/D}{\mathrm{MSCF}/D}$

• • d. Can also be calculated from value of oil and cost of lift gas:
    • i. Example, V_oil=$60/STB C_g=$1/MSCFD

$\begin{array}{cc}{F}_{\mathrm{gu}}=\frac{C_g}{V_{\mathrm{oil}}}=\frac{\frac{1\$}{\mathrm{MSCFD}}}{\frac{\$60}{\mathrm{STB}}}=\frac{1}{60}\left(\frac{\mathrm{STB}}{\mathrm{MSCFD}}\right)& \left(6\right)\end{array}$

The well productivity index J represents the amount of incremental oil production (STB/D) achieved by reducing the flowing bottomhole pressure P_wf by 1 psi. A “straight-line” productivity index is the simplest type, where J is a constant, and it depends on reservoir and fluid properties such as permeability, thickness, viscosity, skin factor, etc. Theoretically, J is only constant for steady-state, single-phase flow, however in some cases it can be approximated as a constant. Alternatively, a Vogel-type inflow performance can be used to determine Jas a function of flowing bottomhole pressure as an approximation to relative permeability effects in multiphase reservoir flow. The gas utilization factor may be specified by an operator based on experience or knowledge of utilization rates in a given field or basin or may be calculated from the value of oil produced (e.g., per STB) and the cost of lift gas.

From the quadratic fit determined from compressor speed/capacity perturbations, the following is known:

$\begin{array}{cc}{P}_s={\mathrm{aS}}^2+\mathrm{bS}+c& \left(7\right)\end{array}$

Where S is compressor speed or injection rate, and a, b, and c are determined. Setting S as q_inf (injection rate) and dividing by K to convert P_s to P_wf (see Eq. 4) it can be shown:

$\begin{array}{cc}\frac{d}{{\mathrm{dq}}_{\mathrm{inj}}}{P}_s*\frac{1}{K}=\frac{2{\mathrm{aq}}_{\mathrm{inj}}+b}{K}& \left(8\right)\end{array}$

Which has units of psi/MSCFD. From this the gas utilization factor can be determined:

$\begin{array}{cc}{F}_{\mathrm{gu}}=\frac{d}{{\mathrm{dq}}_{\mathrm{inj}}}{P}_{\mathrm{wf}}*J=\frac{\left(2{\mathrm{aq}}_{\mathrm{inj}}+b\right)J}{K}& \left(9\right)\end{array}$

This derivative effectively transforms the bottom hole pressure versus gas injection rate graph 250 into a lift gas utilization factor F_gu versus gas injection rate graph 260 as illustrated in FIG. 15. Accordingly, an optimized gas injection rate 262 may be selected based on the desired lift gas utilization factor in a manner substantially identical to the process set forth above.

All directional references (e.g., plus, minus, upper, lower, upward, downward, left, right, leftward, rightward, top, bottom, above, below, vertical, horizontal, clockwise, and counterclockwise) are only used for identification purposes to aid the reader's understanding of the present disclosure, and do not create limitations, particularly as to the position, orientation, or use of the any aspect of the disclosure. As used herein, the phrased “configured to,” “configured for,” and similar phrases indicate that the subject device, apparatus, or system is designed and/or constructed (e.g., through appropriate hardware, software, and/or components) to fulfill one or more specific object purposes, not that the subject device, apparatus, or system is merely capable of performing the object purpose. Joinder references (e.g., attached, coupled, connected, and the like) are to be construed broadly and may include intermediate members between a connection of elements and relative movement between elements. As such, joinder references do not necessarily infer that two elements are directly connected and in fixed relation to each other. It is intended that all matter contained in the above description or shown in the accompanying drawings shall be interpreted as illustrative only and not limiting. Changes in detail or structure may be made without departing from the spirit of the invention as defined in the appended claims.

Any patent, publication, or other disclosure material, in whole or in part, that is said to be incorporated by reference herein is incorporated herein only to the extent that the incorporated materials does not conflict with existing definitions, statements, or other disclosure material set forth in this disclosure. As such, and to the extent necessary, the disclosure as explicitly set forth herein supersedes any conflicting material incorporated herein by reference. Any material, or portion thereof, that is said to be incorporated by reference herein, but which conflicts with existing definitions, statements, or other disclosure material set forth herein will only be incorporated to the extent that no conflict arises between that incorporated material and the existing disclosure material.

Claims

1. A method for controlling a High Pressure Gas Lift (HPGL) system, comprising:

operating a compressor of the HPGL system at at least three gas injection set points for predetermined time periods, wherein the at least three gas injection set points each correspond to one of at least three gas injection rates of the compressor, respectively;

identifying three discharge pressures corresponding to the at least three gas injection set points;

performing a non-linear regression with the values of the at least three discharge pressures relative to the three gas injection set points and identifying a minimum of the non-linear regression;

identifying a gas injection rate associated with the minimum of the non-linear regression;

generating an updated gas injection set point for the compressor based on the gas injection rate associated with the minimum of the non-linear regression; and

operating the compressor at the updated gas injection set point for a predetermined run-time.

2. The method of claim 1, after the operating of the compressor at the updated gas injection set point for the predetermined run-time, further comprising:

identifying a current discharge pressure for the compressor for a current injection set point of the compressor;

perturbing operation of the compressor to run at one or more additional gas injection set points for the predetermined time periods;

identifying one or more discharge pressures for the one or more additional gas injection set points;

determine a further updated gas injection set point based on the one or more discharge pressures; and

operating the compressor at the further updated gas injection set point for another predetermined run-time.

3. The method of claim 2, wherein perturbing operating of the compressor comprises:

increasing or decreasing the current injection set point of the compressor a predetermined amount for predetermined time period and identifying the discharge pressure for the increased or decreased injection rate.

4. The method of claim 2, wherein determining a further updated gas injection set point comprises:

performing a non-linear regression with the values of the current discharge pressure and the one or more discharge pressures relative to their corresponding gas injection set points and identifying a minimum of the non-linear regression;

identifying a updated gas injection rate associated with the minimum of the non-linear regression; and

generating a further updated gas injection set point for the compressor based on the updated gas injection rate associated with a minimum of the non-linear regression.

5. The method of claim 1, wherein the generating of the updated gas injection set point for the compressor based on the gas injection rate associated with the minimum of the non-linear regression:

determining a minimum of the non-linear regression exceeds the maximum injection rate of the compressor; and

setting the updated gas injection set point to a maximum gas injection rate for the compressor.

6. The method of claim 1, wherein the generating of the updated gas injection set point for the compressor based on the gas injection rate associated with the minimum of the non-linear regression comprises:

determining a minimum of the non-linear regression is less than the maximum injection rate of the compressor; and

setting the updated gas injection set point to an injection rate equal to the gas injection rate associated with the minimum of the non-linear regression.

7. The method of claim 1, wherein the generating of the updated gas injection set point for the compressor based on the gas injection rate associated with the minimum of the non-linear regression comprises:

determining the minimum of the non-linear regression is less than the maximum injection rate of the compressor; and

setting the updated gas injection set point a desired gas utilization factor calculated from the gas injection rate associated with the minimum of the non-linear regression.

8. The method of claim 1, wherein the gas injection set points comprise compressor speeds.

9. The method of claim 1, wherein the gas injection set points comprise compressor capacities.

10. The method of claim 1, wherein the gas injection set points comprise gas utilization factors.

11. The method of claim 1, wherein the compressor injects gas into an annulus of a well between casing and production tubing.

12. The method of claim 1, wherein the compressor injects gas into the production tubing of a well.

13. A method for controlling a High Pressure Gas Lift (HPGL) system based on a gas utilization factor, comprising:

operating a compressor of the HPGL system at three gas injection set points for predetermined time periods, wherein the three gas injection set points each correspond to one of three gas injection rates of the compressor, respectively;

identifying three discharge pressures corresponding to the three gas injection set points;

performing a non-linear regression with the values of the three discharge pressures relative to the three gas injection set points to generate a bottom hole pressure verses injection pressure function;

converting the bottom hole pressure function to a gas utilization factor versus injection rate function based on a preselected well productivity index; and

identifying an updated gas injection set point for the compressor using the gas utilization factor versus injection rate function for a predetermined gas lift utilization factor; and

operating the compressor at the updated gas injection set point for a predetermined run-time.

14. The method of claim 13, wherein the gas utilization factor is calculated from an oil value and a cost of lift gas.

15. The method of claim 13, wherein the well productivity index if a function of reservoir properties.

16. The method of claim 13, after the operating of the compressor at the updated gas injection set point for the predetermined run-time, further comprising:

identifying a current discharge pressure for the compressor for a current injection set point of the compressor;

perturbing operation of the compressor to run at one or more additional gas injection set points for the predetermined time periods;

identifying one or more discharge pressures for the one or more additional gas injection set points;

determine a further updated gas injection set point based on the one or more discharge pressures; and

operating the compressor at the further updated gas injection set point for another predetermined run-time.

17. The method of claim 16, wherein perturbing operating of the compressor comprises:

increasing or decreasing the current injection set point of the compressor a predetermined amount for predetermined time period and identifying the discharge pressure for the increased or decreased injection rate.

18. The method of claim 16, wherein determining a further updated gas injection set point comprises:

performing a non-linear regression with the values of the current discharge pressure and the one or more discharge pressures relative to their corresponding gas injection set points and generating an updated bottom hole pressure verses injection pressure function;

converting the updated bottom hole pressure function to an updated gas utilization factor versus injection rate function based on the preselected well productivity index; and

identifying a further updated gas injection set point for the compressor using the gas utilization factor versus injection rate function for the predetermined gas lift utilization factor.

19. The method of claim 13, wherein the gas injection set points comprise compressor speeds.

20. The method of claim 13, wherein the gas injection set points comprise compressor capacities.

21. A High Pressure Gas Lift (HPGL) system, comprising:

at least one HPGL compressor configured to selectively inject gas into one of production tubing and an annulus of a well;

at least one pressure sensor configured to measure a discharge pressure of the at least one HPGL compressor; and

a controller configured to: operate the compressor of the at least one HPGL compressor at three gas injection set points for predetermined time periods, wherein the three gas injection set points each correspond to one of three gas injection rates of the compressor, respectively; identify three discharge pressures corresponding to the three gas injection set points; performing a non-linear regression with the three discharge pressures relative to the three gas injection set points and identifying a minimum of the non-linear regression; identify a gas injection rate associated with the minimum of the non-linear regression; generate an updated gas injection set point for the at least one HPGL compressor based on the gas injection rate associated with the vertex of the quadratic equation; and operate the at least one HPGL compressor at the updated gas injection set point for a predetermined run-time.

22. The system of claim 1, wherein the controller is further configured, after the operating of the compressor at the updated gas injection set point for the predetermined run-time, to:

identify a current discharge pressure for the at least one HPGL compressor for a current injection set point of the compressor;

perturb operation of the at least one HPGL compressor to run at one or more additional gas injection set points for the predetermined time periods;

identify one or more discharge pressures for the one or more additional gas injection set points;

determine a further updated gas injection set point based on the one or more discharge pressures; and

operating the at least one HPGL compressor at the further updated gas injection set point for another predetermined run-time.