[0001] Oilfield operations generate a great deal of electronic data. Such data may be used to access oilfield conditions and make decisions concerning future oilfield operations such as well planning, well targeting, well completions, production rates, and other operations and/or operating parameters. Often this information is used to determine when (and/or where) to drill new wells, re-complete existing wells, or alter oilfield production parameters.

[0002] Oilfield data may be collected using sensors positioned about the oilfield.

For example, sensors on the surface may monitor seismic exploration activities, sensors in the drilling equipment may monitor drilling conditions, sensors in the wellbore may monitor fluid composition, sensors located along the flow path may monitor flow rates, and sensors at the processing facility may monitor fluids collected.

[0003] Computer modeling and simulation of oilfield data is a vital component of oil and gas exploration. Such reservoir simulation systems typically conduct some form of computational processing upon acquired and/or simulated oilfield data and then export the processed data to one or more data visualization application(s) for review by authorized personnel.

[0004] Simulation results may be subjected to one or more optimization algorithms in order to optimize oil production and/or reduce costs associated with the oilfield operation. Unfortunately, known optimization platforms may require the reservoir simulation to be interrupted and restarted at each control step, resulting in delays and file handling inefficiencies. [0005] As such, there remains a need for a system, method and computer readable medium capable of optimizing an oilfield reservoir simulation in an efficient manner.

SUMMARY

[0006] Accordingly, the present disclosure describes a system, method, and computer readable medium capable of generating sequentially optimized simulation results efficiently and effectively using an integrated reservoir simulation and optimization framework.

[0007] In one embodiment, a reservoir simulation computer model may be populated with oilfield data and initialized in order to simulate the flow of fluids through a given subterranean formation for a given period of time. In addition to this "base" simulation, one or more scenarios of the simulation may be populated and initiated as well.

[0008] In one embodiment, the reservoir simulation computer model (along with any number of scenarios) may be cloned and the cloned copy of the simulation (along with any number of scenarios) may be stored without interrupting the base simulation. In one embodiment, optimization parameters may be selected and/or entered into the system in order to control how the optimization is applied to the cloned simulation/scenarios, including the use of customizable optimization time frames.

[0009] Once the optimization parameters have been chosen (or selected by default), the system may apply one or more optimization schemes to the cloned simulation/scenarios. One or more optimization algorithms may be utilized in order to minimize or maximize an objective function and vary one or more control values based upon the results. The control values may then be used to update the oilfield operation and/or the simulation, resulting in optimized oilfield performance.

[0010] This summary is provided to introduce a selection of concepts in a simplified form that are further described herein. 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 determining the scope of the claimed subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

[0011] A more complete appreciation of the invention and many of the attendant advantages thereof will be readily obtained as the same becomes better understood by reference to the following detailed description when considered in connection with the accompanying drawings; it being understood that the drawings contained herein are not necessarily drawn to scale and that the accompanying drawings provide illustrative implementations and are not meant to limit the scope of various technologies described herein; wherein:

[0012] Figure 1.1 is an example oilfield survey operation being performed by a seismic truck.

[0013] Figure 1.2 is an example oilfield drilling operation being performed by a drilling tool suspended by a rig and advanced into the subterranean formation.

[0014] Figure 1.3 is an example oilfield wireline operation being performed by a wireline tool suspended by the rig and into the wellbore of Figure 1.2.

[0015] Figure 1.4 is an example oilfield operation being performed by a production tool deployed from the rig and into a completed wellbore for drawing fluid from the downhole reservoir into a surface facility.

[0016] Figure 2.1 is an example oilfield seismic trace of the subterranean formation of Figure 1.1.

[0017] Figure 2.2 is an example oilfield core sample of the example formation shown in Figure 1.2.

[0018] Figure 2.3 is an example oilfield well log of the subterranean formation of

Figure 1.3.

[0019] Figure 2.4 is an example simulation decline curve of fluid flowing through the example subterranean formation of Figure 1.4. [0020] Figure 3 is a schematic view, partially in cross section, of an example oilfield operation having a plurality of data acquisition tools positioned at various locations along the oilfield operation for collecting data from the subterranean formation.

[0021] Figure 4 is an example schematic view of an oilfield operation having a plurality of wellsites for producing hydrocarbons from the subterranean formation.

[0022] Figure 5 is a flowchart diagram illustrating an oilfield optimization process of one example embodiment.

[0023] Figure 6 is a conceptual diagram illustrating an oilfield optimization process of one example embodiment.

[0024] Figure 7 is a graphical representation of an oilfield illustrating oil saturation and well flow control status.

[0025] Figures 8A-8D are graphical representations of optimization results of one example embodiment.

[0026] Figures 9A-9F are graphical representations of optimization results of one example embodiment.

[0027] Figure 10 is a graphical representation of objective function optimization results of one example embodiment.

[0028] Figures 11A-11F are graphical representations of objective function and control value optimization results of one example embodiment.

[0029] Figure 12 is an example computer system that may be utilized in conjunction with one or more embodiments.

DESCRIPTION

[0030] In the following description, numerous details are set forth to provide an understanding of the present invention. However, it will be understood by those skilled in the art that the inventions described herein may be practiced without these details and that numerous variations or modifications from the described embodiments may be possible. [0031] The present disclosure describes embodiments of a method of optimizing an oilfield operation, a computer readable medium for optimizing an oilfield operation and an oilfield optimization system.

[0032] By way of background, Figures 1.1-1.4 illustrate simplified, schematic views of oilfield (100) having subterranean formation (102) containing reservoir (104) therein in accordance with implementations of various technologies and techniques described herein.

[0033] Figure 1.1 illustrates a survey operation being performed by a survey tool, such as seismic truck (106.1), to measure properties of the subterranean formation. In this example, the survey operation is a seismic survey operation for producing sound vibrations. In Figure 1.1, sound vibrations (112) generated by source (110), reflects off horizons (114) in earth formation (1 16). A set of sound vibrations is received by sensors, such as geophone-receivers (118), situated on the earth's surface. The data received (120) is provided as input data to a computer (122.1) of a seismic truck (106.1), and responsive to the input data, computer (122.1) generates seismic data output (124). This seismic data output may be stored, transmitted or further processed as desired, for example, by data reduction.

[0034] Figure 1.2 illustrates a drilling operation being performed by drilling tool

(106.2) suspended by rig (128) and advanced into subterranean formations (102) to form wellbore (136). Mud pit (130) is used to draw drilling mud into the drilling tools via flow line (132) for circulating drilling mud down through the drilling tools, then up wellbore (136) and back to the surface. The drilling mud may be filtered and returned to the mud pit.

[0035] A circulating system may be used for storing, controlling, or filtering the drilling mud. The drilling tools are advanced into subterranean formations (102) to reach reservoir (104). Each well may target one or more reservoirs. The drilling tools may be adapted for measuring downhole properties using logging while drilling tools. The logging while drilling tools may also be adapted for taking core sample (133). [0036] Computer facilities may be positioned at various locations about the oilfield (100) (e.g., the surface unit 134) and/or at remote locations. Surface unit (134) may be used to communicate with the drilling tools and/or offsite operations, as well as with other surface or downhole sensors. Surface unit is capable of communicating with the drilling tools to send commands to the drilling tools, and to receive data therefrom. Surface unit may also collect data generated during the drilling operation and produce data output (135), which may then be stored or transmitted.

[0037] Sensors (S), such as gauges, may be positioned about oilfield (100) to collect data relating to various oilfield operations as described previously. In this example, sensor (S) may be positioned in one or more locations in the drilling tools and/or at rig (128) to measure drilling parameters, such as weight on bit, torque on bit, pressures, temperatures, flow rates, compositions, rotary speed, and/or other parameters of the field operation. Sensors (S) may also be positioned in one or more locations in the circulating system.

[0038] Drilling tools (106.2) may include a bottom hole assembly (BHA) (not shown) near the drill bit (e.g., within several drill collar lengths from the drill bit). The bottom hole assembly may include capabilities for measuring, processing, and storing information, as well as communicating with the surface unit. The bottom hole assembly may further include drill collars for performing various other measurement functions.

[0039] The data gathered by sensors (S) may be collected by the surface unit and/or other data collection sources for analysis or other processing. The data collected by sensors (S) may be used alone or in combination with other data. The data may be collected in one or more databases and/or transmitted on or offsite. The data may be historical data, real time data, or combinations thereof. The real time data may be used in real time, or stored for later use. The data may also be combined with historical data or other inputs for further analysis. The data may be stored in separate databases, or combined into a single database.

[0040] Surface unit (134) may include transceiver (137) to allow communications between surface unit (134) and various portions of the oilfield (100) or other locations. The surface unit may also be provided with one or more controllers (not shown) for actuating and/or adjusting mechanisms at the oilfield. The surface unit may then send command signals to the oilfield in response to data received.

[0041] The surface unit may receive commands via transceiver (137) or may itself execute commands to the controller. A processor may be provided to analyze the data (locally or remotely), make the decisions and/or actuate the controller. In this manner, the oilfield may be selectively adjusted based on the data that is collected and analyzed. This technique may be used to optimize portions of the field operation, such as controlling drilling, weight on bit, pump rates, or other parameters such as the control values described in greater detail below. These adjustments may be made automatically based on computer protocol, and/or manually by an operator. In some cases, well plans may be adjusted to select optimum operating conditions, or to avoid problems.

[0042] Figure 1.3 illustrates a wireline operation being performed by wireline tool

(106.3) suspended by rig (128) and into wellbore (136) of Figure 1.2. The wireline tool may be adapted for deployment into the wellbore for generating well logs, performing downhole tests and/or collecting samples. The wireline tool may be used to provide another method and apparatus for performing a seismic survey operation. The wireline tool may, for example, have an explosive, radioactive, electrical, or acoustic energy source (144) that sends and/or receives electrical signals to surrounding subterranean formations (102) and fluids therein.

[0043] Wireline tool (106.3) may be operatively connected to, for example, geophones (118) and a computer (122.1) of a seismic truck (106.1) of Figure 1.1. Wireline tool (106.3) may also provide data to surface unit (134). Surface unit (134) may collect data generated during the wireline operation and may produce data output (135) that may be stored or transmitted and subsequently analyzed. Wireline tool (106.3) may be positioned at various depths in the wellbore (136) to provide information relating to the subterranean formation (102).

[0044] Sensors (S), such as gauges, may be positioned about oilfield (100) to collect data relating to various field operations as described previously. Sensors may be positioned in wireline tool (106.3) to measure downhole parameters which relate to, for example porosity, permeability, fluid composition and/or other parameters of the oilfield operation.

[0045] Figure 1.4 illustrates a production operation being performed by production tool (106.4) deployed from a production unit or Christmas tree (129) and into completed wellbore (136) for drawing fluid from the downhole reservoirs into surface facilities (142). The fluid flows from reservoir (104) through perforations in the casing (not shown) and into production tool (106.4) in wellbore (136) and to surface facilities (142) via gathering network (146).

[0046] Sensors, such as gauges, may be positioned about oilfield (100) to collect data relating to various field operations as described previously. Sensors may be positioned in production tool (106.4) or associated equipment, such as Christmas tree (129), gathering network (146), surface facility (142), and/or the production facility, to measure fluid parameters, such as fluid composition, flow rates, pressures, temperatures, and/or other parameters of the production operation.

[0047] Production may also include injection wells for added recovery. One or more gathering facilities may be operatively connected to one or more of the wellsites for selectively collecting downhole fluids from the wellsite(s).

[0048] While Figures 1.2-1.4 illustrate tools used to measure data relating to an oilfield, it will be appreciated that the tools may be used in connection with non-oilfield operations, such as gas fields, mines, aquifers, storage, or other subterranean facilities. Also, while certain data acquisition tools are depicted, it will be appreciated that various measurement tools capable of sensing parameters, such as seismic two-way travel time, density, resistivity, production rate, etc., of the subterranean formation and/or its geological formations may be used. Various sensors (S) may be located at various positions along the wellbore and/or the monitoring tools to collect and/or monitor the desired data. Other sources of data may also be provided from offsite locations. [0049] Figures 2.1-2 A are example graphical depictions of data collected by the tools of Figures 1.1-1.4. Figure. 2.1 depicts a seismic trace (202) of the subterranean formation of Figure 1.1 taken by survey truck (106.1). The seismic trace measures a two- way response over a period of time. Figure 2.2 depicts a core sample (233) taken by the drilling tool (106.2). The core test may provide a graph of the density, resistivity, or other physical property of the core sample (233) over the length of the core. Tests for density and viscosity may be performed on the fluids in the core at varying pressures and temperatures. Figure 2.3 depicts a well log (204) of the subterranean formation of Figure 1.3 taken by the wireline tool (106.3). The wireline log typically provides a resistivity measurement of the formation at various depths. Figure 2.4 depicts a production decline curve (206) of fluid flowing through the subterranean formation of Figure 1.4 taken by the production tool (106.4). The production decline curve (206) may provide the production rate Q as a function of time t.

[0050] The respective graphs of Figures 2.1-2.3 contain static measurements that describe the physical characteristics of the formation. These measurements may be compared to determine the accuracy of the measurements and/or for checking for errors. In this manner, the plots of each of the respective measurements may be aligned and scaled for comparison and verification of the properties.

[0051] Figure 2.4 provides a dynamic measurement of the fluid properties through the wellbore. As the fluid flows through the wellbore, measurements are taken of fluid properties, such as flow rates, pressures, composition, etc. As described below, the static and dynamic measurements may be used to generate models of the subterranean formation to determine characteristics thereof.

[0052] Figure 3 is a schematic view, partially in cross section of an oilfield (300) having data acquisition tools (302A), (302B), (302C), and (302D) positioned at various locations along the oilfield for collecting data of a subterranean formation (304). The data acquisition tools (302A-302D) may be the same as data acquisition tools of Figure 1, respectively. In this example, the data acquisition tools (302A-302D) may generate data plots or measurements (308A-308D), respectively. [0053] Data plots (308A-308D) are examples of static data plots that may be generated by the data acquisition tools (302A-302D), respectively. Static data plot (308A) is a seismic two-way response time and may be the same as the seismic trace (202) of Figure 2.1. Static plot (308B) is core sample data measured from a core sample of the formation (304), similar to the core sample (233) of Figure 2.2. Static data plot (308C) is a logging trace, similar to the well log (204) of Figure 2.3. Data plot (308D) is a dynamic data plot of the fluid flow rate over time, similar to the graph (206) of Figure 2.4. Other data may also be collected, such as historical data, user inputs, economic information, other measurement data, and other parameters of interest.

[0054] The subterranean formation (304) has a plurality of geological structures

(306A-306D). In this example, the formation has a sandstone layer (306A), a limestone layer (306B), a shale layer (306C), and a sand layer (306D). A fault line (307) extends through the formation. The static data acquisition tools may be adapted to measure the formation and detect the characteristics of the geological structures of the formation.

[0055] While a specific subterranean formation (304) with specific geological structures are depicted, it will be appreciated that the formation may contain a variety of geological structures. Fluid may also be present in various portions of the formation. Each of the measurement devices may be used to measure properties of the formation and/or its underlying structures in order to generate oilfield data. While each acquisition tool is shown as being in specific locations along the formation, it will be appreciated that one or more types of measurement may be taken at one or more location across one or more oilfields or other locations for comparison and/or analysis.

[0056] The data collected from various sources, such as the data acquisition tools of Figure 3, may then be evaluated using one or more data analysis applications. Seismic data displayed in the static data plot (308A) from the data acquisition tool (302A) may be used by a geophysicist to determine characteristics of the subterranean formation (304). Core data shown in static plot (308B) and/or log data from the well log (308C) may be used by a geologist to determine various characteristics of the geological structures of the subterranean formation (304). Production data from the production graph (308D) may be used by the reservoir engineer to determine fluid flow reservoir characteristics.

[0057] Figure 4 illustrates an example oilfield (400) for performing oilfield operations. In this example, the oilfield has a plurality of wellsites (402) operatively connected to a central processing facility (454). Part or all of the oilfield may be on land and/or sea. Also, while a single oilfield with a single processing facility and a plurality of wellsites is depicted, any combination of one or more oilfields, one or more processing facilities and one or more wellsites may be present.

[0058] Each wellsite (402) may have equipment that forms a wellbore (436) into the earth. The wellbores extend through subterranean formations (406) including reservoirs (404). These reservoirs (404) contain fluids, such as hydrocarbons. The wellsites draw fluid from the reservoirs and pass them to the processing facilities via surface networks (444). The surface networks (444) may have tubing and control mechanisms for controlling the flow of fluids from the wellsite to the processing facility (454).

[0059] Referring to Figure 5, the present disclosure describes a system, method, and computer readable medium capable of optimizing an oilfield operation using one or more reservoir simulation computer models. In one embodiment, one or more computer databases (500) may be utilized for storing oilfield data (505) relating to one or more oilfield operations (510). Oilfield data may include sensor data (515) obtained from one or more sensors (S) positioned throughout the oilfield. Oilfield data may also include data gleaned from one or more supervisory control and data acquisition (SCADA) systems. Oilfield data as described herein may include measured/ob served oilfield data and/or simulated oilfield data generated by one or more computer simulation(s).

[0060] In one embodiment, one or more reservoir simulation computer models

(also referred to herein as "simulations" or "simulators") may be used to analyze oilfield data and predict the flow of fluids (e.g., oil, water, and gas) through porous media in the subterranean formation. Reservoir simulation models may be used to determine where to drill new wells and/or manage existing oilfield facilities in order to maximize production and/or reduce cost.

[0061] In one embodiment, the reservoir simulation computer models may be displayed to the user using a two, three, or four dimensional arrangement. In one embodiment, a two dimensional arrangement may include x and y axis components, a three dimensional arrangement may include x, y, and z components, and a four dimensional arrangement may include x, y, z components along with a time component. Oilfield data may be represented utilizing any number of conventions. For example, various color schemes may be utilized to convey the characteristics of the displayed oilfield data.

[0062] Reservoir simulation models may be subjected to history matching and/or geo-statistical algorithms in order to improve the reliability of the simulation results. History matching involves adjusting the reservoir simulation model until it closely reproduces the past behavior of a real-world oilfield reservoir. The accuracy of the history matching may depend on the quality of the reservoir model and the quality and quantity of available pressure and production data. In one embodiment, a reservoir simulation model may be a stand-alone application, such as the Intersect ^® or Eclipse ^® systems offered by Schlumberger ^® , or a proprietary reservoir simulation package.

[0063] Optimization schemes may be applied to various stages of the simulation in order to maximize and/or minimize a quantity of the simulation. For example, one may wish to maximize quantities such as the net present value, net cash flow and/or oil/gas production of an oilfield operation while minimizing quantities such as water production and/or costs relating to the oilfield operation.

[0064] Unfortunately, known optimization platforms suffer from a number of inefficiencies. First, the optimization platform may be separate from and/or external to the simulator such that it is not capable of communicating directly with the simulation platform. Thus, the reservoir simulation must be halted at each control step in order to allow the optimization scheme to be applied to the simulation, resulting in processing delays. Further, simulation files must be saved when the simulation in halted and restart files must be loaded and updated with the results of the optimization at each control step, resulting in file handling inefficiencies. Second, the optimization platform may require the optimization to be applied to the lifespan of the oilfield operation at each optimization control step. Thus, the optimization requires more time to complete and does not allow the user to specify customized optimization time frames.

[0065] The present disclosure describes a reservoir simulation and optimization framework capable of generating optimized simulation results efficiently and effectively. In one embodiment, the optimization platform may be integrated with the simulation platform such that the two systems work together as one solution, sharing computer code, storage infrastructure and allowing the two systems to sync with each other in real time. Alternatively, a suitable software interface capable of facilitating communication may be utilized in order to provide seamless cooperation between the two systems.

[0066] In one embodiment, a reservoir simulation computer model may be populated with oilfield data and initialized in order to simulate the flow of fluids through a given subterranean formation for a given period of time, as illustrated by Box (520) of Figure 5. In addition to this "base" simulation, one or more scenarios of the simulation may be populated and initiated as well. Scenarios are variations of the base simulation where one or more parameters of the subterranean formation being simulated have been altered.

[0067] Alterations may include changes to the depositional environment and/or structural framework of the subterranean formation at issue. The use of multiple scenarios in addition to the base simulation allows the system to consider a wide range of possibilities regarding how the subterranean formation will behave in the future, thus reducing uncertainty. The use of multiple scenarios is sometimes referred to an "ensemble" approach, in that the system is considering an ensemble or group of simulations as a whole.

[0068] In one embodiment, the reservoir simulation computer model (along with any number of scenarios) may be cloned and the cloned copy of the simulation (along with any number of scenarios) may be stored without interrupting the base simulation, as illustrated by Box (525) of Figure 5. The cloned copy of the simulation (along with any applicable scenarios) may be stored on a local storage device that is shared by both the simulator and the optimizer. This feature allows the system to optimize the simulation (along with any number of scenarios) without interrupting the base simulation.

[0069] In one embodiment, the cloned simulation/scenarios may include a copy of all of the solvers provided by the original simulation/scenarios. Further, the cloned simulation/scenarios may be grid based simulations/scenarios, i.e., simulations containing all of the grid by grid detail of the base simulation. The cloned simulation/scenarios may also be proxy or reduced order simulations, i.e., simulations which include only the most relevant portions (such as well level production/pressure profiles and/or subterranean response surfaces) of the base simulation.

[0070] In one embodiment, optimization parameters may be selected and/or entered into the system in order to control how the optimization is applied to the cloned simulation/scenarios, as illustrated by Box (530) of Figure 5. Further, optimization parameters may be stored by the system and applied by default.

[0071] Referring to Figure 6, in one embodiment, optimization parameters may include control parameters providing, for example, information regarding the number of control steps (535) to be utilized, the time duration (540) to be used during each control step optimization, control values (545) to be utilized during the optimization, etc. Figure 6 illustrates an example optimization process where multiple simulation scenarios (550) are optimized over multiple control steps spanning multiple time frames.

[0072] Optimization parameters may also include information regarding the quantities that the user wishes to optimize. For example, one may wish to maximize quantities such as the net present value, net cash flow and/or oil/gas production of an oilfield operation while minimizing quantities such as water production and/or costs relating to the oilfield operation. In one embodiment, the system allows the user to select the quantity or quantities he or she wishes to optimize for each control step. In one embodiment, the system may provide customization options through which the user may amend default optimization parameters by entering and/or importing custom preferences. In one embodiment, this may be accomplished using one or more customization screens (not shown).

[0073] In one embodiment, one of the optimization parameters may include the type of cloned simulation the user wishes to use for the optimization. As noted above, the cloned simulation/scenario may be a grid based simulation or a proxy simulation. In one embodiment, the user may select which type of simulation they which to use during the cloning process and may select a different type of simulation for each control step if they wish. For example, the user may which to use a proxy simulation instead of the larger grid based simulation where processing time and/or storage space is limited.

[0074] The optimization process described herein is capable of optimizing an oilfield operation using both a minimizing objective function and a maximizing objective function (simultaneously) without interrupting the underlying simulation. In one embodiment, the system may provide a graphic user interface through which the user may indicate that he or she wishes to optimize a simulation (or scenario) using both a maximizing and a minimizing arrangement.

[0075] For example, the system may utilize Ji to represent the objective function to be maximized (net present value in this example) and J2 to represent the objective function to be minimized (water production in this example) with respect to control values "w". Both objective functions may then be evaluated (using the same cloned simulation) in order to generate control values that maximize net present value while also minimizing water production. The user may also indicate which objective function he or she wishes to prioritize using one or more weighting factors that may be applied to each gradient, e.g., Wk+1 = Uk + ai*gradient of Ji - a ₂ *gradient of J ₂ .

[0076] Alternatively, the system may clone the simulation in question twice such that the first cloned simulation (or scenario) may be set up to maximize the objective function selected by the user and the second cloned simulation (or scenario) may be set up to minimize the objective function selected by the user. The results may then be cross referenced and used to generate appropriate control values. [0077] Once the optimization parameters have been chosen (or selected by default), the system may apply one or more optimization schemes to the cloned simulation/scenarios, as illustrated by Box (555) of Figure 5. One or more optimization algorithms may be utilized in order to minimize or maximize an objective function associated with the quantity selected. In one embodiment, the system may use an ensemble-based optimization scheme (EnOpt) described in Chen, Y. and Oliver D. : "Efficient Ensemble-Based Closed-Loop Production Optimization," paper SPE 1 12873 presented at the 2008 SPE/DOE Improved Oil Recovery Symposium, Tulsa, OK, 19-23 April; the entire contents of which are incorporated by reference herein.

[0078] In one embodiment, the objective function may be expressed as a function of one or more control values "u " such as producer and injector well flow control valves. For example, if the user (or the system by default) selects net present value as the value to be maximized, the system may identify net present value as an objective function and use an optimization algorithm to maximize this value with respect to one or more control values associated with the oilfield operation. In one embodiment, the system may use a steepest ascent optimization algorithm to approximate the gradient of the objective function. In order to find the maximum of the objective function, the system may take steps proportional to the positive of the gradient (or of the approximate gradient) of the objective function at the current point.

[0079] Likewise, if the user (or the system by default) selects cost as a value to be minimized, the system may identify cost as an objective function and use an optimization algorithm to minimize this value with respect to one or more control values associated with the oilfield operation. In one embodiment, the system may use a steepest descent optimization algorithm to approximate the gradient of the objective function. In order to find the minimum of the objective function, the system may take steps proportional to the negative of the gradient (or of the approximate gradient) of the objective function at the current point.

[0080] Figure 6 illustrates an example optimization process. In this example, a steepest ascent gradient optimization algorithm may be used to optimize one or more well controls based on ensemble approximation of the gradient using the EnOpt optimization technique (set forth in Chen et al.) where the controls may be optimized sequentially and the forecast of the objective function may be performed by cloning the base simulation (and its accompanying scenarios in this example) and optimizing each of them using a user defined (or default) prediction time frame. Once the optimization loop is complete, the system may move the simulations forward until the next control step in order to perform another optimization loop.

[0081] In one embodiment, the base simulation may be applied to the lifespan or expected duration of the oilfield operation, i.e., from the current time until the oilfield is exhausted or otherwise rendered inoperative. Such simulations may take a great deal of time to complete. As noted above, known optimization platforms may also require the optimization to be applied to the lifespan or expected duration of the oilfield operation at each optimization control step. Thus, the optimization requires more time to complete and does not allow the user to specify custom optimization time frames.

[0082] In one embodiment, the system provides a graphic user interface through which the user may specify custom optimization time frames that are shorter than the lifespan of the oilfield operation, making the optimization process more efficient and cost effective.

[0083] All simulations and optimization results interpretation may be performed in memory on cloned reservoir simulations/scenarios, as noted above. The example process of Figure 6 illustrates an optimizer that drives multiple scenarios of the same reservoir model to perform an ensemble based steepest ascent optimization of the well controls. The scenarios may be generated by applying a history matching algorithm or from applying multiple runs of a geo-statistics algorithm.

[0084] In this example, no restart files are needed to stop and load the simulator at every control step, the system allows the optimization process to proceed over the selected time frame and allows for adjustments to one or more control values with the simulator interactively at every control step. In this example, the system may solve the reservoir and well systems from the control time for a user specified prediction time window to evaluate the objective function J, for example the Net Present Value (NPV):

where:

[0085] The control values, u _k in this example, may be the well rates and pressure controls of an oilfield operation including their flow control device settings. At every control step k the steepest ascent algorithm may evaluate the objective function J(u) on each cloned reservoir model scenario in order to approximate the gradient of J, V/, with respect to the perturbed controls u _k ; once the maximum objective function is achieved, the system may update the well controls to their new value u _k+1 = u _k + aVJ where a is the control step size chosen to maximize the objective function J, as illustrated by Boxes (560 and 565) of Figure 5.

[0086] To illustrate, suppose we have one well producer equipped with a flow control device to control the well flow rate. The oil price is 50 USD and the discount rate is 0.0 and no water is produced. Hence, we have J(u) =∑] SO q°(u) t ^l . The oil rate may be taken as a function of the well flow control valve setting u for example: q° (u) = u/(u ² + 1) where u is the control valve opening setting between 0.0 (fully closed) and 1.0 (fully open). In this example, the maximum of J is at the maximum of q° when u = 1.0. [0087] Now at control step k the optimizer may try different values of u to evaluate the objective function J of every reservoir model scenario by forecasting the cloned reservoir model of each scenario for a defined time window T. Summing up the cumulative oil volumes (q° t ^l ) to compute the objective function J of each scenario, the cross-covariance of the objective function J with the controls u will approximate the gradient of J with respect to u as explained by Chen et al, then the steepest ascent algorithm may update the well controls to their new values u _k+1 . Once the maximum of J is found the optimizer may apply the optimal controls u to the wells and proceed to next control step.

[0088] The system may update control values at the oilfield operation, at the base simulation and/or at each of the cloned simulation/scenarios. Further, the system may continue generating and updating control values for each control step by resetting the control step and applying the updated values to the next control step, as illustrated by Boxes (570 and 575) of Figure 5. Once all control steps have been completed, the system may delete or otherwise dispose of the cloned simulations/scenarios, further reducing file handling requirements, as illustrated by Box (580) of Figure 5. In one embodiment, the updated control values may be provided to the surface unit (134) or other suitable functionality which may, in turn, actuate and/or adjust one or more well control valves (or other oilfield equipment) in use at the oilfield operation.

[0089] In one embodiment, user selections and/or simulation/optimization preferences may be stored for later projects. For example, if a user has made selections for a particular oilfield operation or simulation/optimization project, the system may store preference information for the user and/or project in question and apply it to later sessions.

[0090] Figure 7 provides an illustration of an example black oil synthetic 3D model arranged into 58x70x15 grid blocks (60900 cells). The model in this example has 6 producer wells and 2 water injector wells. The production wells in this example have a constraint of 8000 reservoir cubic meters per day (RM ³ /day) maximum liquid reservoir volume production rate limit, and 90 bars minimum bottom hole pressure. The injector wells in this example have a constraint of 8000 RM ³ /day maximum reservoir volume injection rate limit and 330 bars maximum bottom hole pressure limit. The objective function in this example is the undiscounted net present value of the oilfield operation and it may be evaluated using live cloned reservoir simulation(s)/scenario(s) at every control step: inj

j =∑ (∑;:f (^ - > -∑

i ^ v where:

[0091] In this example, the optimization may be executed once every 30 days of the simulation, controlling the producer wells surface liquid production rate and the injector wells surface water injection rate. Further, the control values may be updated at every control step following the iterative steepest ascent gradient algorithm. Five scenarios of the same reservoir model were used in this example to evaluate the well's response and compute the mean and covariance of the objective function with respect to the controls in this example.

[0092] Figures 8A-8D (respectively) illustrate the cumulative oil production for the field (FOPT), the cumulative water production for the field (FWPT), the cumulative water injection rate for the field (FWIT) and the net present value in optimized and unoptimized cases in this example. Specifically, Figures 8A-8D illustrate that the net present value may be maximized with an oil price of $60 and a water cost of $5.00 and that both the water production and the water injection rates are reduced in optimized cases.

[0093] In another example, a black oil synthetic 2D model arranged into 90 grid blocks in the XZ plane may be utilized. The permeability of the reservoir may be constant and set to 500 mD in the X direction and 100 mD in the Z direction. The model may have 2 wells, one producer and one water injector well located at the opposite edges of the reservoir grid. The production well may be completed in layers 2, 5 and 8 with a flow control valve mounted at a segment of layer 8; while the injector may be completed in layers 2 and 10 with a flow control valve mounted on the well at the segment of layer 10 at the bottom of the reservoir.

[0094] The production well in this example may have a 3000 reservoir barrels per day (Rb/day) maximum reservoir volume production rate limit and a minimum bottom hole pressure limit of 100 psi, while the injector well may have a 3000 Rb/day maximum reservoir volume injection rate limit and a 3500 psi maximum bottom hole pressure limit. The objective function in this example may be the Undiscounted Net Cash Flow (UNCF) of the field and may be evaluated at every control step using the following formulae: where:

[0095] The optimization may be executed once every 30 days of the simulation in this example. The wells flow control valve settings may vary between 0 (fully closed) and 1.0 (fully open), and may be updated at every control step following the application of the iterative steepest ascent gradient algorithm. The steepest ascent search direction (gradient) may be approximated by the ensemble cross-covariance of the objective function with respect to the control values.

[0096] In this example, we ran 30 and 50 iterations of the reservoir simulation model were conducted and used to evaluate the well's response by computing the objective function mean and its covaraince with respect to the controls. Figures 9A-9F illustrate the cumulative oil and water production and their rates that maximize the UNCF with an oil price of $100 and a water cost of $5. Figures 9A-9F further illustrate an improvement in cumulative oil production and a reduced water production rate for the optimized cases. Also, one can see that between 500 and 1500 days, most of the gas is produced in the optimized case, leading to faster reservoir depletion than in the base case, and that the production well is under bottom hole pressure control earlier. In this example, better control of the gas production at the top layers of the formation may lead to an improved production profile.

[0097] Figure 10 illustrates the objective function (UNCF in this example) at various stages of the optimization and that the objective function increases over time due to the optimization scheme described herein.

[0098] Figures 11A-1 1F illustrate the effect of each control value setting on the objective function. In this example, the UNCF is maximized when the producer valve is closed while the injection valve setting appears to have little impact.

[0099] The system, method and computer readable medium described herein may be utilized in conjunction with any suitable simulation/optimization package and the inventions described herein are not limited to use with the examples provided herein. Further, the inventions described herein may be used at any phase of an oilfield operation including, but not limited to, during the interpretation of seismic data, during modeling of formational characteristics or reservoir properties (including surface modeling), during operational monitoring and analysis activities and/or during production forecasting operations. [00100] The methods described herein may be implemented on any suitable computer system capable of processing electronic data. Figure 12 illustrates one possible configuration of a computer system (590) that may be utilized. Computer system(s), such as the example system of Figure 12, may run programs containing instructions, that, when executed, perform methods according to the principles described herein. Furthermore, the methods described herein may be fully automated and able to operate continuously, as desired.

[00101] The computer system may utilize one or more central processing units

(595), memory (600), communications or I/O modules (605), graphics devices (610), a floating point accelerator (615), and mass storage devices such as tapes and discs (620). Storage device (620) may include a floppy drive, hard drive, CD-ROM, optical drive, or any other form of storage device. In addition, the storage devices may be capable of receiving a floppy disk, CD-ROM, DVD-ROM, disk, flash drive or any other form of computer-readable medium that may contain computer-executable instructions.

[00102] Further, communication device (605) may be a modem, network card, or any other device to enable communication to receive and/or transmit data. It should be understood that the computer system (590) may include a plurality of interconnected (whether by intranet or Internet) computer systems, including without limitation, personal computers, mainframes, PDAs, cell phones and the like.

[00103] It should be understood that the various technologies described herein may be implemented in connection with hardware, software or a combination of both. Thus, various technologies, or certain aspects or portions thereof, may take the form of program code (i.e., instructions) embodied in tangible media, such as floppy diskettes, CD-ROMs, hard drives, or any other machine-readable storage medium wherein, when the program code is loaded into and executed by a machine, such as a computer, the machine becomes an apparatus for practicing the various technologies.

[00104] In the case of program code execution on programmable computers, the computing device may include a processor, a storage medium readable by the processor (including volatile and non-volatile memory and/or storage elements), at least one input device, and at least one output device. One or more programs that may implement or utilize the various technologies described herein may use an application programming interface (API), reusable controls, and the like.

[00105] Such programs may be implemented in a high level procedural or object oriented programming language to communicate with a computer system. However, the program(s) may be implemented in assembly or machine language, if desired. In any case, the language may be a compiled or interpreted language, and combined with hardware implementations.

[00106] The computer system (590) may include hardware capable of executing machine readable instructions, as well as the software for executing acts that produce a desired result. In addition, computer system (590) may include hybrids of hardware and software, as well as computer sub-systems.

[00107] Hardware may include at least processor-capable platforms, such as client- machines (also known as personal computers or servers), and hand-held processing devices (such as smart phones, personal digital assistants (PDAs), or personal computing devices (PCDs), for example). Further, hardware may include any physical device that is capable of storing machine-readable instructions, such as memory or other data storage devices. Other forms of hardware include hardware sub-systems, including transfer devices such as modems, modem cards, ports, and port cards, for example.

[00108] Software includes any machine code stored in any memory medium, such as RAM or ROM, and machine code stored on other devices (such as floppy disks, flash memory, or a CD ROM, for example). Software may include source or object code, for example. In addition, software encompasses any set of instructions capable of being executed in a client machine or server.

[00109] A database may be any standard or proprietary database software, such as Oracle, Microsoft Access, SyBase, or DBase II, for example. The database may have fields, records, data, and other database elements that may be associated through database specific software. Additionally, data may be mapped. Mapping is the process of associating one data entry with another data entry. For example, the data contained in the location of a character file can be mapped to a field in a second table. The physical location of the database is not limiting, and the database may be distributed. For example, the database may exist remotely from the server, and run on a separate platform.

[00110] Further, the computer system may operate in a networked environment using logical connections to one or more remote computers. The logical connections may be any connection that is commonplace in offices, enterprise-wide computer networks, intranets, and the Internet, such as local area network (LAN) and a wide area network (WAN). The remote computers may each include one or more application programs.

[00111] When using a LAN networking environment, the computer system may be connected to the local network through a network interface or adapter. When used in a WAN networking environment, the computer system may include a modem, wireless router or other means for establishing communication over a wide area network, such as the Internet.

[00112] The modem, which may be internal or external, may be connected to the system bus via the serial port interface. In a networked environment, program modules depicted relative to the computer system, or portions thereof, may be stored in a remote memory storage device.

[00113] Although the invention has been described with reference to specific embodiments, this description is not meant to be construed in a limited sense. Various modifications of the disclosed embodiments, as well as alternative embodiments of the invention, will become apparent to persons skilled in the art upon reference to the description of the invention. It is, therefore, contemplated that the appended claims will cover such modifications that fall within the scope of the invention.