EP2948618B1 - Optimisation contrainte pour planification de positionnement de puits - Google Patents

Optimisation contrainte pour planification de positionnement de puits Download PDF

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Publication number
EP2948618B1
EP2948618B1 EP14743637.2A EP14743637A EP2948618B1 EP 2948618 B1 EP2948618 B1 EP 2948618B1 EP 14743637 A EP14743637 A EP 14743637A EP 2948618 B1 EP2948618 B1 EP 2948618B1
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Prior art keywords
well placement
placement plan
control vector
feasibility
candidate well
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German (de)
English (en)
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EP2948618A1 (fr
EP2948618A4 (fr
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Peter Gerhard Tilke
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Services Petroliers Schlumberger SA
Schlumberger Technology BV
Schlumberger Holdings Ltd
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Services Petroliers Schlumberger SA
Schlumberger Technology BV
Schlumberger Holdings Ltd
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    • E—FIXED CONSTRUCTIONS
    • E21—EARTH OR ROCK DRILLING; MINING
    • E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
    • E21B43/00—Methods or apparatus for obtaining oil, gas, water, soluble or meltable materials or a slurry of minerals from wells
    • E21B43/30—Specific pattern of wells, e.g. optimising the spacing of wells
    • E21B43/305—Specific pattern of wells, e.g. optimising the spacing of wells comprising at least one inclined or horizontal well

Definitions

  • Well placement planning is used in a number of industries to plan out the placement of prospective wells.
  • well placement planning is used to select placements and trajectories for proposed wells into a subsurface reservoir to reach specific locations in the reservoir that are believed to contain recoverable hydrocarbons.
  • Well placement planning may be used to produce a well placement plan (WPP) that includes one or more wells, as well as additional information such as well trajectories, well completions, drilling schedules, etc.
  • WPP well placement plan
  • a reservoir simulator is used in connection with well placement planning so that reservoir simulation may be performed to determine the potential value of any well placement plan.
  • One example can be found in the document US2008120148 A1 , considered as the closest prior art.
  • Well placement planning may generally be considered to be an optimization problem.
  • well placement planning has been performed in a predominantly manual process in which a user selects target and well locations, performs a reservoir simulation forecast, and then calculates a value based on the forecast oil or gas recovered and the cost of the wells.
  • the user generally may repeat the process a number of times, but modify the number and location of the wells and completions.
  • the modifications may include, for example, different water flooding strategies, well spacing, well types, platform locations, etc.
  • Well placement planning has been found to be a very time-consuming process from both the user's perspective and a computational perspective.
  • Well placement planning has also been found to be a relatively inefficient process because it may be difficult for a user to objectively explore the complete solution space.
  • US2008/120148 refers to a client-server based system for building and executing flows (i.e., interconnected systems of algorithms).
  • the client allows a user to build a flow specification and send the flow specification to the server.
  • the server assembles the flow from the flow spec and executes the flow.
  • a flow may be configured to analyze the impact (e.g. the financial impact) of a number of uncertainties associated with a plurality of assets.
  • Uncertainty variables are used to characterize the uncertainties associated with the assets.
  • An uncertainty variable associated with one asset may be functionally dependent on an uncertainty variable associated with another asset.
  • the embodiments disclosed herein provide a method, apparatus, and program product that utilize constrained optimization framework to generate a well placement plan based on a reservoir model.
  • Candidate well placement plans are generated from control vectors proposed by an optimization engine to optimize based upon an objective function that generally involves an access to a reservoir simulator.
  • Constraints that are not based on computation of the objective function referred to herein as inexpensive constraints, are evaluated prior to computation of the objective function (e.g., by accessing the reservoir simulator) to avoid unnecessary computationally expensive operations for candidate well placement plans determined to be infeasible in view of the inexpensive constraints.
  • the objective function may be calculated and additional constraints, referred to herein as expensive constraints, may thereafter be evaluated to further determine the feasibility of candidate well placement plans.
  • a method for well placement planning includes generating a control vector comprising a plurality of control variables over which to optimize, translating the control vector to a candidate well placement plan, performing a first feasibility evaluation for the candidate well placement plan against one or more inexpensive constraints, and in response to determining a feasibility of the candidate well placement plan from the first feasibility evaluation, computing a result for an objective function based upon the candidate well placement plan using a reservoir simulator and performing a second feasibility evaluation for the candidate well placement plan by evaluating the computed result for the objective function based upon the candidate well placement plan against one or more expensive constraints.
  • an apparatus includes at least one processing unit and program code configured upon execution by the at least one processing unit to perform well placement planning by generating a control vector comprising a plurality of control variables over which to optimize, translating the control vector to a candidate well placement plan, performing a first feasibility evaluation for the candidate well placement plan against one or more inexpensive constraints, and in response to determining a feasibility of the candidate well placement plan from the first feasibility evaluation, computing a result for an objective function based upon the candidate well placement plan using a reservoir simulator and performing a second feasibility evaluation for the candidate well placement plan by evaluating the computed result for the objective function based upon the candidate well placement plan against one or more expensive constraints.
  • a program product includes a computer readable medium and program code stored on the computer readable medium and configured upon execution by at least one processing unit to perform well placement planning by generating a control vector comprising a plurality of control variables over which to optimize, translating the control vector to a candidate well placement plan, performing a first feasibility evaluation for the candidate well placement plan against one or more inexpensive constraints, and in response to determining a feasibility of the candidate well placement plan from the first feasibility evaluation, computing a result for an objective function based upon the candidate well placement plan using a reservoir simulator and performing a second feasibility evaluation for the candidate well placement plan by evaluating the computed result for the objective function based upon the candidate well placement plan against one or more expensive constraints.
  • an apparatus includes at least one processing unit, program code and means for performing well placement planning by generating a control vector comprising a plurality of control variables over which to optimize, translating the control vector to a candidate well placement plan, performing a first feasibility evaluation for the candidate well placement plan against one or more inexpensive constraints, and in response to determining a feasibility of the candidate well placement plan from the first feasibility evaluation, computing a result for an objective function based upon the candidate well placement plan using a reservoir simulator and performing a second feasibility evaluation for the candidate well placement plan by evaluating the computed result for the objective function based upon the candidate well placement plan against one or more expensive constraints.
  • an aspect of the application involves performing a feasibility evaluation for the control vector against one or more linear constraints prior to translating the control vector, where translating the control vector is only performed in response to determining a feasibility of the control vector from the third feasibility evaluation.
  • an aspect of the application includes that the control vector comprises an initial control vector, and involves generating the initial control vector by translating an initial well placement plan to the initial control vector.
  • an aspect of the application involves, in response to determining an infeasibility of the candidate well placement plan from the first feasibility evaluation, bypassing computing the result for the objective function and performing the second feasibility evaluation.
  • an aspect of the application involves, in response to determining a feasibility of the candidate well placement plan from the second feasibility evaluation, determining that the candidate well placement plan is a feasible well placement plan.
  • an aspect of the application involves, for each of a plurality of control vectors, performing a trial processing operation associated therewith, where each trial processing operation comprises determining feasibility for the associated control vector against one or more linear constraints and, in response to determining a feasibility of the associated control vector against the one or more linear constraints, translating the associated control vector to an associated candidate well placement plan, performing the first feasibility evaluation for the associated candidate well placement plan against the one or more inexpensive constraints, and in response to determining a feasibility of the associated candidate well placement plan from the first feasibility evaluation, computing a result for the objective function based upon the associated candidate well placement plan using the reservoir simulator, and performing the second feasibility evaluation for the associated candidate well placement plan by evaluating the computed result for the objective function based upon the associated candidate well placement plan against the one or more expensive constraints.
  • an aspect of the application involves generating at least one of the plurality of control vectors by extrapolating from a prior control vector based at least in part on a feasibility evaluation performed during a trial processing operation for the prior control vector.
  • an aspect of the application includes that the prior control vector is associated with an associated candidate well placement plan determined as infeasible, and extrapolating from the prior control vector is based upon a result of at least one feasibility evaluation performed during the trial processing operation for the prior control vector.
  • an aspect of the application involves terminating well placement planning after performing the trial processing operation for each of the plurality of control vectors in response to a termination condition, where the termination condition is based on a determination that a maximum number of trial processing operations have been performed, a determination that improvement in the objective function has stalled, or a combination thereof.
  • an aspect of the application includes that the reservoir simulator comprises an analytical reservoir simulator that accesses a coarse scale reservoir simulation model.
  • an aspect of the application involves generating the coarse scale reservoir simulation model by upscaling a fine scale reservoir geology model.
  • an aspect of the application includes that the objective function includes one or more of net present value, return on investment, profitability, production index, or combinations thereof.
  • an aspect of the application includes that computing the result of the objective function comprises computing a plurality of results for a plurality of realizations to account for uncertainty in the reservoir model, the method further comprising optimizing on a utility function based on the plurality of results computed for the plurality of realizations.
  • an aspect of the application includes that translating the control vector to the candidate well placement plan comprises identifying a plurality of target locations in a reservoir, determining a completion geometry for each target location, and determining a trajectory for each target location.
  • an aspect of the application includes that determining the completion geometry for a first target location among the plurality of target locations comprises determining at least one completion location based upon at least one property of a plurality of cells associated with the first target location and retrieved from a fine scale reservoir geology model.
  • an aspect of the includes that the one or more inexpensive constraints includes a feasibility of the first target location based on a geometric relation to the fine scale reservoir geology model, where the geometric relation includes a minimum completion length, a minimum standoff relative to a fluid contact, a minimum distance to a fault, or a combination thereof.
  • an aspect of the application includes that the one or more inexpensive constraints includes a feasibility of the first target location based on a property of the fine scale reservoir geology model, where the property includes minimum porosity, minimum permeability, maximum water saturation, or a combination thereof.
  • an aspect of the application includes that performing the first feasibility evaluation for the candidate well placement plan against the one or more inexpensive constraints comprises performing anti-collision analysis on the candidate well placement plan.
  • an aspect of the application includes that the one or more inexpensive constraints includes one or more of dogleg severity, maximum inclination, maximum reach, number of platforms, number of wells, flowing producers, slot number, platform location, minimum tie point separation, minimum completion spacing, or combinations thereof.
  • an aspect of the application includes that the one or more expensive constraints includes one or more of sub-economic wells, flowing producers or a combination thereof.
  • an aspect of the application includes that the control vector comprises one or more of target location coordinates, tie point coordinates, azimuth of a pattern, pattern spacing, or combinations thereof.
  • the herein-described embodiments provide a method, apparatus, and program product that implement a constrained optimization framework to generate a well placement plan based on a reservoir model.
  • Candidate well placement plans are generated from control vectors proposed by an optimization engine to optimize based upon an objective function that generally involves an access to a reservoir simulator.
  • Constraints that are not based on computation of the objective function referred to herein as inexpensive constraints, are evaluated prior to accessing the reservoir simulator to avoid unnecessary accesses to the reservoir simulator for candidate well placement plans determined to be infeasible in view of the inexpensive constraints.
  • the objective function may be calculated and additional constraints, referred to herein as expensive constraints, may thereafter be evaluated to further determine the feasibility of candidate well placement plans.
  • a well placement plan also referred to as a field development plan
  • a field development plan may be considered to include one or more wells proposed for a geographic region such as an oilfield, as well as additional planning information associated with drilling and completing the wells, including, for example, location and/or trajectory information, completion information, drilling schedule information, projected production information, or any other information suitable for use in drilling the proposed wells.
  • a constrained optimization framework may be considered to include a framework through which a constrained optimization approach may be applied to the generation of a well placement plan (WPP) in the presence of uncertainty and risk, based upon one or more reservoir models, and based upon a set of constraints that drive the feasibility of candidate well placement plans developed by the framework.
  • Constraints may be geometric, operational, contractual and/or legal in nature, and as discussed in greater detail below, may vary in terms of their computational expense. Inexpensive constraints, for example, may generally be considered to include constraints that may be evaluated without accessing a reservoir simulator, while expensive constraints may generally be considered to include constraints that do involve an access to a reservoir simulator prior to evaluation.
  • one or more reservoir simulators are used in the illustrated embodiments in the computation of an objective function that drives the optimization to a desired end result, e.g., to maximize net present value, return on investment, profitability, production, etc.
  • well placement plans are associated with control vectors that are used to calculate the objective function for different well placement plans.
  • FIG. 1 illustrates an example data processing system 10 in which the various technologies and techniques described herein may be implemented.
  • System 10 is illustrated as including one or more computers 12, e.g., client computers, each including a central processing unit (CPU) 14 including at least one hardware-based processor or processing core 16.
  • CPU 14 is coupled to a memory 18, which may represent the random access memory (RAM) devices comprising the main storage of a computer 12, as well as any supplemental levels of memory, e.g., cache memories, non-volatile or backup memories (e.g., programmable or flash memories), read-only memories, etc.
  • RAM random access memory
  • memory 18 may be considered to include memory storage physically located elsewhere in a computer 12, e.g., any cache memory in a microprocessor or processing core, as well as any storage capacity used as a virtual memory, e.g., as stored on a mass storage device 20 or on another computer coupled to a computer 12.
  • Each computer 12 also generally receives a number of inputs and outputs for communicating information externally.
  • a computer 12 generally includes a user interface 22 incorporating one or more user input/output devices, e.g., a keyboard, a pointing device, a display, a printer, etc. Otherwise, user input may be received, e.g., over a network interface 24 coupled to a network 26, from one or more external computers, e.g., one or more servers 28 or other computers 12.
  • a computer 12 also may be in communication with one or more mass storage devices 20, which may be, for example, internal hard disk storage devices, external hard disk storage devices, storage area network devices, etc.
  • a computer 12 generally operates under the control of an operating system 30 and executes or otherwise relies upon various computer software applications, components, programs, objects, modules, data structures, etc.
  • a petro-technical module or component 32 executing within an exploration and production (E&P) platform 34 may be used to access, process, generate, modify or otherwise utilize petro-technical data, e.g., as stored locally in a database 36 and/or accessible remotely from a collaboration platform 38.
  • Collaboration platform 38 may be implemented using multiple servers 28 in some implementations, and it will be appreciated that each server 28 may incorporate a CPU, memory, and other hardware components similar to a computer 12.
  • E&P platform 34 may implemented as the PETREL Exploration & Production (E&P) software platform
  • collaboration platform 38 may be implemented as the STUDIO E&P KNOWLEDGE ENVIRONMENT platform, both of which are available from Schlumberger Ltd. and its affiliates. It will be appreciated, however, that the techniques discussed herein may be utilized in connection with other platforms and environments, so the application is not limited to the particular software platforms and environments discussed herein.
  • routines executed to implement the embodiments disclosed herein will be referred to herein as "computer program code,” or simply “program code.”
  • Program code generally comprises one or more instructions that are resident at various times in various memory and storage devices in a computer, and that, when read and executed by one or more hardware-based processing units in a computer (e.g., microprocessors, processing cores or other hardware-based circuit logic), cause that computer to perform the steps embodying desired functionality.
  • Computer readable media may include computer readable storage media and communication media.
  • Computer readable storage media is non-transitory in nature, and may include volatile and non-volatile, and removable and non-removable media implemented in any method or technology for storage of information, such as computer-readable instructions, data structures, program modules or other data.
  • Computer readable storage media may further include RAM, ROM, erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other solid state memory technology, CD-ROM, DVD, or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and which can be accessed by computer 10.
  • Communication media may embody computer readable instructions, data structures or other program modules.
  • communication media may include wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media. Combinations of any of the above may also be included within the scope of computer readable media.
  • Figs. 2A-2D illustrate simplified, schematic views of an oilfield 100 having subterranean formation 102 containing reservoir 104 therein in accordance with implementations of various technologies and techniques described herein.
  • Fig. 2A illustrates a survey operation being performed by a survey tool, such as seismic truck 106.1, to measure properties of the subterranean formation.
  • the survey operation is a seismic survey operation for producing sound vibrations.
  • one such sound vibration, sound vibration 112 generated by source 110 reflects off horizons 114 in earth formation 116.
  • 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.
  • Fig. 2B illustrates a drilling operation being performed by drilling tools 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.
  • a circulating system may be used for storing, controlling or filtering the flowing drilling muds.
  • the drilling tools are advanced into subterranean formations 102 to reach reservoir 104. Each well may target one or more reservoirs.
  • the drilling tools are adapted for measuring downhole properties using logging while drilling tools.
  • the logging while drilling tools may also be adapted for taking core sample 133 as shown.
  • 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 134 is capable of communicating with the drilling tools to send commands to the drilling tools, and to receive data therefrom.
  • Surface unit 134 may also collect data generated during the drilling operation and produces data output 135, which may then be stored or transmitted.
  • Sensors (S), such as gauges, may be positioned about oilfield 100 to collect data relating to various oilfield operations as described previously. As shown, sensor (S) is 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.
  • Drilling tools 106.2 may include a bottom hole assembly (BHA) (not shown), generally referenced, near the drill bit (e.g., within several drill collar lengths from the drill bit).
  • BHA bottom hole assembly
  • the bottom hole assembly includes capabilities for measuring, processing, and storing information, as well as communicating with surface unit 134.
  • the bottom hole assembly further includes drill collars for performing various other measurement functions.
  • the bottom hole assembly may include a communication subassembly that communicates with surface unit 134.
  • the communication subassembly is adapted to send signals to and receive signals from the surface using a communications channel such as mud pulse telemetry, electro-magnetic telemetry, or wired drill pipe communications.
  • the communication subassembly may include, for example, a transmitter that generates a signal, such as an acoustic or electromagnetic signal, which is representative of the measured drilling parameters. It will be appreciated by one of skill in the art that a variety of telemetry systems may be employed, such as wired drill pipe, electromagnetic or other known telemetry systems.
  • the wellbore is drilled according to a drilling plan that is established prior to drilling.
  • the drilling plan sets forth equipment, pressures, trajectories and/or other parameters that define the drilling process for the wellsite.
  • the drilling operation may then be performed according to the drilling plan. However, as information is gathered, the drilling operation may need to deviate from the drilling plan. Additionally, as drilling or other operations are performed, the subsurface conditions may change.
  • the earth model may also need adjustment as new information is collected
  • the data gathered by sensors (S) may be collected by surface unit 134 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.
  • Surface unit 134 may include transceiver 137 to allow communications between surface unit 134 and various portions of the oilfield 100 or other locations.
  • Surface unit 134 may also be provided with or functionally connected to one or more controllers (not shown) for actuating mechanisms at oilfield 100.
  • Surface unit 134 may then send command signals to oilfield 100 in response to data received.
  • Surface unit 134 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, oilfield 100 may be selectively adjusted based on the data collected. This technique may be used to optimize portions of the field operation, such as controlling drilling, weight on bit, pump rates or other parameters. 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.
  • Fig. 2C illustrates a wireline operation being performed by wireline tool 106.3 suspended by rig 128 and into wellbore 136 of Fig. 2B .
  • Wireline tool 106.3 is adapted for deployment into wellbore 136 for generating well logs, performing downhole tests and/or collecting samples.
  • Wireline tool 106.3 may be used to provide another method and apparatus for performing a seismic survey operation.
  • Wireline tool 106.3 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.
  • 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 Fig. 2A .
  • 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.
  • Wireline tool 106.3 may be positioned at various depths in the wellbore 136 to provide a survey or other information relating to the subterranean formation 102.
  • Sensors such as gauges, may be positioned about oilfield 100 to collect data relating to various field operations as described previously. As shown, sensor S is 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 field operation.
  • Fig. 2D 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.
  • Sensors such as gauges, may be positioned about oilfield 100 to collect data relating to various field operations as described previously. As shown, the sensor (S) 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.
  • 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.
  • 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).
  • Figs. 2B-2D illustrate tools used to measure properties of an oilfield
  • the tools may be used in connection with non-oilfield operations, such as gas fields, mines, aquifers, storage, or other subterranean facilities.
  • non-oilfield operations such as gas fields, mines, aquifers, storage, or other subterranean facilities.
  • 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.
  • Figs. 2A-2D are intended to provide a brief description of an example of a field usable with oilfield application frameworks.
  • Part, or all, of oilfield 100 may be on land, water, and/or sea.
  • oilfield applications may be utilized with any combination of one or more oilfields, one or more processing facilities and one or more wellsites.
  • Fig. 3 illustrates a schematic view, partially in cross section of oilfield 200 having data acquisition tools 202.1, 202.2, 202.3 and 202.4 positioned at various locations along oilfield 200 for collecting data of subterranean formation 204 in accordance with implementations of various technologies and techniques described herein.
  • Data acquisition tools 202.1-202.4 may be the same as data acquisition tools 106.1-106.4 of Figs. 2A-2D , respectively, or others not depicted.
  • data acquisition tools 202.1-202.4 generate data plots or measurements 208.1-208.4, respectively. These data plots are depicted along oilfield 200 to demonstrate the data generated by the various operations.
  • Data plots 208.1-208.3 are examples of static data plots that may be generated by data acquisition tools 202.1-202.3, respectively, however, it should be understood that data plots 208.1-208.3 may also be data plots that are updated in real time. These measurements may be analyzed to better define the properties of the formation(s) and/or determine the accuracy of the measurements and/or for checking for errors. The plots of each of the respective measurements may be aligned and scaled for comparison and verification of the properties.
  • Static data plot 208.1 is a seismic two-way response over a period of time.
  • Static plot 208.2 is core sample data measured from a core sample of the formation 204.
  • the core sample may be used to provide data, such as a graph of the density, porosity, permeability, or some other physical property of the core sample 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.
  • Static data plot 208.3 is a logging trace that generally provides a resistivity or other measurement of the formation at various depths.
  • a production decline curve or graph 208.4 is a dynamic data plot of the fluid flow rate over time.
  • the production decline curve generally provides the production rate as a function of time.
  • measurements are taken of fluid properties, such as flow rates, pressures, composition, etc.
  • Other data may also be collected, such as historical data, user inputs, economic information, and/or other measurement data and other parameters of interest.
  • the static and dynamic measurements may be analyzed and used to generate models of the subterranean formation to determine characteristics thereof. Similar measurements may also be used to measure changes in formation aspects over time.
  • the subterranean structure 204 has a plurality of geological formations 206.1-206.4. As shown, this structure has several formations or layers, including a shale layer 206.1, a carbonate layer 206.2, a shale layer 206.3 and a sand layer 206.4. A fault 207 extends through the shale layer 206.1 and the carbonate layer 206.2.
  • the static data acquisition tools are adapted to take measurements and detect characteristics of the formations.
  • oilfield 200 may contain a variety of geological structures and/or formations, sometimes having extreme complexity. In some locations, generally below the water line, fluid may occupy pore spaces of the formations.
  • Each of the measurement devices may be used to measure properties of the formations and/or its geological features. While each acquisition tool is shown as being in specific locations in oilfield 200, it will be appreciated that one or more types of measurement may be taken at one or more locations across one or more fields or other locations for comparison and/or analysis.
  • seismic data displayed in static data plot 208.1 from data acquisition tool 202.1 is used by a geophysicist to determine characteristics of the subterranean formations and features.
  • the core data shown in static plot 208.2 and/or log data from well log 208.3 are generally used by a geologist to determine various characteristics of the subterranean formation.
  • the production data from graph 208.4 is generally used by the reservoir engineer to determine fluid flow reservoir characteristics.
  • the data analyzed by the geologist, geophysicist and the reservoir engineer may be analyzed using modeling techniques.
  • Fig. 4 illustrates an oilfield 300 for performing production operations in accordance with implementations of various technologies and techniques described herein.
  • the oilfield has a plurality of wellsites 302 operatively connected to central processing facility 354.
  • the oilfield configuration of Fig. 4 is not intended to limit the scope of the oilfield application system. 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.
  • Each wellsite 302 has equipment that forms wellbore 336 into the earth.
  • the wellbores extend through subterranean formations 306 including reservoirs 304.
  • These reservoirs 304 contain fluids, such as hydrocarbons.
  • the wellsites draw fluid from the reservoirs and pass them to the processing facilities via surface networks 344.
  • the surface networks 344 have tubing and control mechanisms for controlling the flow of fluids from the wellsite to processing facility 354.
  • Embodiments consistent with the application may be used to facilitate well placement planning through the use of an optimization framework that applies a constrained optimization approach to generate an optimal well placement plan based upon an objective function representing a desired end goal, e.g., net present value, profitability, return on investment, production, etc.
  • an optimization framework that applies a constrained optimization approach to generate an optimal well placement plan based upon an objective function representing a desired end goal, e.g., net present value, profitability, return on investment, production, etc.
  • well placement planning is an optimization problem. It involves discovering the optimal wells and completions to attempt to maximize the value of an asset.
  • f : R n ⁇ R is the objective function, l,u the lower and upper bounds respectively, g : R n ⁇ R q the inequality constraints, and h : R n ⁇ R m the inequality constraints.
  • the constraint functions (g, h) may be linear or non-linear with respect to the control variables.
  • a number of approaches exist for discovering the optimal set of control variables x also referred to herein as a control vector that optimizes the objective function.
  • well placement may be treated as an integer or a mixed integer problem in which all or some of the control variables assume integer values, e.g., if all drilling targets are known.
  • the control variables generally assume continuous real values that cannot be treated as an integer or mixed integer problem.
  • control variables being continuous
  • well placement optimization problems generally have computationally complex objective and constraint functions for which simple functional forms are generally not available.
  • this problem generally will also not have derivatives of the objective and constraint functions available, because the analytical form generally cannot be obtained and the numerical form may be too noisy to be useful.
  • a derivative free optimization approach e.g., a nonlinear downhill simplex pattern search algorithm or a stochastic optimization algorithm
  • Other optimization techniques include Genetic Algorithms (GA), Simulated Annealing (SA), Branch and Bound (B&B), Covariance Matrix Adaptation-Evolution Strategy (CMA-ES), Particle Swarm Optimization (PSO), Spontaneous Perturbation Stochastic Approximation (SPSA), Retrospective Optimization using Hooke Jeeves search (ROHJ), Nelder-Mead downhill Simplex (N-M), or Generalized Reduced Gradient (GRG) Genetic, among others.
  • GA Genetic Algorithms
  • SA Simulated Annealing
  • B&B Branch and Bound
  • CMA-ES Covariance Matrix Adaptation-Evolution Strategy
  • PSO Particle Swarm Optimization
  • SPSA Spontaneous Perturbation Stochastic Approximation
  • ROHJ Retrospective Optimization using Hooke Jeeves
  • an optimization engine generally proposes a control vector, and the objective function is evaluated.
  • the algorithm then proposes a new "trial" of the control vector using information from the results of previous trials, with the goal of selecting a control vector that improves the value of the objective function.
  • the optimization generally terminates when the maximum number of trials has been evaluated or a desired accuracy of the objective function and control vector values has been reached.
  • the safeguards may include, for example, generating a good initial guess so that the downhill simplex engine has a good starting point, and when an optimum solution has been found, the optimal control vector can be used as an initial guess for a repeat optimization, with such nested optimizations optionally repeated until no substantial improvement in the optimum is found.
  • the general downhill simplex method is an unconstrained optimization technique in which the elements of the control vector x are unbounded.
  • well placement optimization has been found to be a highly constrained problem in which the control vector elements are not only bounded as shown in equation (1) but also subjected to linear and non-linear constraints as shown in equations (2) and (3).
  • a sequential lexicographic approach may be used, where the original problem is reformulated into another minimization problem in which the original objective function f(x) is minimized subject to ⁇ (x) ⁇ 0, where the constraint violation function ⁇ (x) is strictly positive for infeasible control vectors and less than or equal to zero for feasible ones, that is: ⁇ x > 0 if x ⁇ ⁇ x ⁇ 0 if x ⁇ where is the feasible region.
  • control vectors may be compared using the lexicographic order comparison operator ( ⁇ CL ) rather than simple comparison of the objective function values, that is: f 1 , ⁇ 1 ⁇ C L f 2 , ⁇ 2 ⁇ ⁇ i f x 1 ⁇ ⁇ x 2 ⁇ : ⁇ 1 ⁇ ⁇ 2 e l s e f 1 ⁇ f 2
  • Inexpensive constraints may be considered to be constraints for which the feasibility can be determined before the objective function is evaluated or otherwise without using results of the objective function in the determinations, while expensive constraints may be considered to be constraints determined after the objective function is evaluated or otherwise using results of the objective function in the determinations.
  • ⁇ SL lexicographic sequential order comparison operator
  • Workflow 400 may utilize a framework that automatically generates an optimal Well Placement Plan (WPP) based on a reservoir model, and in the illustrated embodiment a suite of high-speed computational components generally allows a WPP to be generated quickly (e.g., in minutes).
  • WPP Well Placement Plan
  • Workflow 400 may be used to automate the process of placing new wells in a reservoir and/or sidetracking or recompleting existing wells, and does so using constraint-based optimization techniques.
  • optimization of a WPP using one embodiment of workflow 400 may utilize a constrained downhill simplex approach.
  • WPP's proposed by an optimization engine in earlier trials may be extrapolated to propose a new WPP.
  • a proposed WPP may be evaluated for satisfying a range of geometric, operational, contractual and legal constraints on the surface, and in the overburden and reservoir. Collision and hazard avoidance computation may also use a geocomputation topology approach.
  • a production forecast may be computed using high-speed (e.g., in seconds) reservoir simulator that analytically computes pressure and explicitly computes saturation.
  • additional objective functions e.g., Net Present Value, Return on Investment, Profitability Index, Maintain Production Rate, etc. may also be used. Optimization in the presence of subsurface uncertainty may also be considered by using an ensemble of reservoir models.
  • workflow 400 is dominated by a loop that generally involves the creation of a control vector by an optimization engine, the translation of this control vector into a WPP, the feasibility constraints analysis of that WPP, and the evaluation of the objective function for the WPP.
  • a single pass through the loop is termed a "trial,” and this sequence of steps is termed a trial processing operation or element.
  • the optimization engine in this case, the constrained downhill simplex discussed previously, then proposes a new control vector with the intention of discovering an optimal control vector.
  • the optimization loop is then complete when one or more termination conditions is satisfied.
  • Workflow 400 may be implemented, for example, at least in part within petro-technical module 32 of Fig. 1 , which may be implemented as, or otherwise access an optimization engine. Module 32 may also access one or more reservoir simulators (e.g., resident in E&P platform 34) for use in accessing one or more reservoir models. It will be appreciated by those of ordinary skill in the art having the benefit of the instant disclosure that some operations in workflow 400 may be combined, split, reordered, omitted and/or supplemented with other techniques known in the art, and therefore, the application is not limited to the particular workflow illustrated in Fig. 5 .
  • workflow 400 may incorporate some initialization operations, including, as illustrated in block 402, a reservoir upscaling operation.
  • the reservoir upscaling operation may be performed, for example, to upscale one or more fine scale or high resolution geology models 404 to generate a coarse scale or low resolution simulation model suitable for use by an analytical reservoir simulator when computing an objective function, such that computation of the objective function may be performed using a high-speed (e.g., in seconds) reservoir simulation.
  • Additional initialization operations e.g., parsing existing wells and geologic hazards in the overburden for collision avoidance, may also be performed.
  • block 402 passes control to block 408 to generate an initial guess control vector 410, which is then processed by a trial processing element 412, which upon completion of a trial, passes control to block 414 to generate another control vector 410.
  • Control vectors and their associated trial results including feasibility or infeasibility with respect to various constraints and the magnitudes of such feasibility/infeasibility, may also be maintained in a database or other data storage as illustrated at 416.
  • the optimization engine in general may be unaware of the domain and physical meaning of each control variable. It is, however, one role of the trial processing element 412 of the workflow to analyze the control vector, generate a WPP and inform the optimization engine of the feasibility and objective function values.
  • an initial guess control vector may be generated from an initial WPP from candidate target and platform tie point locations, in an operation that is effectively the inverse of generating a WPP from a control vector (which is performed in block 420, discussed below).
  • Targets for the initial control vector may be selected with criteria under a user's control. For example, it may be favorable to use targets near the crest of anticlines, or focus on regions with the maximum productivity index, or minimum water saturation. Other manners of generating an initial control vector will be appreciated by one of ordinary skill in the art having the benefit of the instant disclosure.
  • a trial is initiated for a control vector by performing a feasibility evaluation for the control vector against one or more linear constraints in block 418.
  • control variables may map directly to tie point or target locations, so in these cases, the control variables' values may be transformed directly into project coordinates and evaluated for inclusion or exclusion in the project's region of interest. If a control vector is determined to be infeasible as a result of this evaluation, trial processing ends for the control vector and control passes to block 414 to generate a new control vector.
  • control vector advances to the next stage of creating a candidate WPP, as illustrated by block 420, which may also be referred to as translating the control vector into a candidate WPP.
  • target identification, trajectory creation and completion creation are performed for one or more wells based upon the control variables in the control vector to generate a WPP 422.
  • Target identification generally refers to identification of target locations in a reservoir.
  • some of the control variables in a control vector may correspond directly to targeted locations (X, Y).
  • the high-resolution, or fine scale, geological model 404 may be analyzed to extract the cells corresponding to each targeted location (e.g., as illustrated by effective porosity and water saturation columns 450, 452 in Fig. 6 ). For a vertical well, this generally corresponds to the cells including the X, Y coordinate. It will be appreciated that the extraction of cells, and in particular, the properties associated with such cells, is substantially less computationally-expensive than running a numerical simulation with a high-resolution geological model. Consequently, high resolution reservoir data may be accessed in connection with generating a WPP in a computationally-efficient manner.
  • completion geometry corresponding to each location may also be identified.
  • a user may supply constraints that are used in the construction of the completion. For example, to be feasible, a completion generally has a minimum length and a minimum standoff from a fluid contact (e.g., as shown by completions 454, 456 and 458 in Fig. 6 ). Cells may also have valid properties such as minimum permeability, or maximum water saturation. Generally, a completion is created if these criteria are satisfied.
  • the control vector generally includes either explicit or implicit tie point location information. For example, if an existing platform is to be used for a trajectory, the tie point will be part of the problem definition and not included in the control vector. A trajectory may then be constructed which connects the tie point to the target (e.g., as illustrated by trajectory 460 coupled to target 462 in Fig. 7 ).
  • block 424 then performs an evaluation of the WPP against one or more inexpensive constraints.
  • the inexpensive constraints may be constraints on the WPP that may be evaluated without computing the objective function.
  • one type of inexpensive constraint is related to anti-collision.
  • a brownfield by definition contains existing wells, and as these existing wells may be actively flowing, abandoned, or a combination, when new wells are proposed it may be desirable to perform anti-collision or hazard avoidance analysis to evaluated whether any well trajectories collide with existing wells or other hazards in the reservoir (e.g., natural hazards).
  • An anti-collision analysis may be implemented, for example, in the manner disclosed in U.S. Provisional Application No. 61/756,789 filed on January 25, 2013 by Peter Tilke . Such analysis may therefore be performed in connection with feasibility constraint evaluations to ensure the wells in a WPP avoid existing wells and other hazards.
  • Another type of inexpensive constraint may be related to a trajectory. For example, dogleg severity, maximum inclination and maximum reach may be used to limit the tie points that may feasibly connect with a target.
  • Another type of inexpensive constraint may be evaluated for a target location based on one or more geometric relations between the target location and the high resolution reservoir geology model. These geometric relations may include, but are not limited to, geometric relations such as minimum completion length, minimum standoff relative to a fluid contact, minimum distance to a fault, or combinations thereof. Yet another type of inexpensive constraint may be evaluated for a target location based on one or more properties of the high resolution reservoir geology model. These properties may include, but are not limited to, minimum porosity, minimum permeability, maximum water saturation or combinations thereof.
  • Additional inexpensive constraints may include:
  • block 424 terminates the trial for the current candidate control vector and returns control to block 414 to generate a new control vector. As such, the computational expense of computing the objective function for this WPP is avoided.
  • block 424 passes control to block 426 to compute the objective function.
  • optimization conventionally seeks to discover the feasible control vector yielding the minimum objective function value.
  • the desire is to maximize an objective function value.
  • the computed value is negated before returning the value to the optimization engine.
  • one objective may be to simply maximize recovery, in which case capital and operating costs along with oil or gas price may be ignored. This may also be the case if the objective is to maintain a plateau production rate.
  • a more complete financial objective function may be used in some embodiments to calculate net present value (NPV) in which a forecast recovery, a commodity price, and the costs are considered along with a discount factor.
  • NPV net present value
  • Other objective functions include, for example, fiscal parameters such as return on investment (ROI) and profitability index.
  • Costs may be separated into capital and operating expenses.
  • Capital expenses may include drilling, and surface facility, drilling, well, and completion construction.
  • Operating expenses may include personnel, injection, production and treatment costs.
  • the one component that adds value to the objective function is the oil or gas recovered from the reservoir, and everything else is cost. While a user may provide an estimate of a forecast commodity price, the production forecast itself generally is computed.
  • the objective function is computed in block 426 whenever the proposed WPP in a trial satisfies the inexpensive constraints. Otherwise, computation of the objective function, and evaluation of expensive constraints (discussed below) are bypassed. From a computational perspective the objective function computation, e.g., a production forecast calculation, is generally the most computationally expensive part of a trial. For this reason, a high-speed analytical reservoir simulator, utilizing coarse scale model 406, may be used to compute the forecast.
  • the simulator may be subject to initial and boundary conditions. Iso-parametric transformation may be used to extend the solution to irregular non-cuboid reservoirs.
  • a pressure analytical saturation explicit (PASE) method may be used to extend the solution to waterflooding problems.
  • block 426 passes control to block 428 to perform a feasibility evaluation of the candidate WPP against a set of expensive constraints.
  • the objective function may be possible in some embodiments that some wells in the WPP are flowing at sub-economic rates. The WPP may therefore be evaluated to remove sub-economic wells. The WPP may then be evaluated to ensure that flowing producers still remain in the solution.
  • control returns to block 414 to generate a new control vector. Otherwise, the WPP is added to a set of feasible WPP's 430, and control passes to block 432 to determine whether the optimization is complete. If not, control passes to block 414 to generate another control vector. If so, control passes to block 434 to terminate the workflow and return results to the user.
  • Trial processing element 412 may therefore be repeated by the optimization engine until an optimal solution is discovered, or otherwise until another termination condition is met.
  • optimization engine uses information garnered from control vectors, both infeasible and feasible, to extrapolate new control vectors from past trials.
  • feasible control vectors are reported back as results to the user, representing the viable well placement plans determined from the well placement planning workflow.
  • Fig. 8 illustrates a plot of objective function results (here, value) computed for a plurality of trials.
  • the plot of Fig. 8 may be progressively generated and displayed to a user during the workflow, with updates made for each feasible WPP added to the results. As such, a user may view the improvement in the objective function over the course of the workflow.
  • Fig. 8 also illustrates at about trial 60 where the optimization reaches a plateau and supplies the optimum as a new initial guess for a new "restarted" optimization that eventually yields a more-improved value, just one type of potential optimization technique that may be used by an optimization engine consistent with the invention.
  • Block 432 may terminate workflow 400 in response to different termination conditions.
  • a termination condition may be based on a determination that a maximum specified number of trials has been completed.
  • a termination condition may be based on achieving an objective function value that ceases to improve with successive trials within a specified accuracy, or put another way, a determination that improvement in the objective function has stalled (e.g., insufficient improvement has occurred over a most recent set of trials as prescribed by a tolerance).
  • a combination of determinations may be made, e.g., to terminate after the objective function does not improve more than X% over the last Y trials, but in any event never exceed Z total trials.
  • Embodiments consistent with the application may also optimize in the presence of uncertainty.
  • uncertain optimization an optimal control vector is being sought when the underlying model is uncertain.
  • the model may be represented by the overburden and the reservoir, and during optimization, the uncertainty in the model may be reflected in an uncertainty in the objective function value.
  • the overall optimization workflow may remain the same, and function in essentially the same manner as illustrated in Fig. 5 as with deterministic optimization.
  • the value of the objective function being minimized may be considered to be a function of the uncertainty distribution in the objective function value.
  • the objective function value may have statistical moments such as mean ( ⁇ ) and variance ( ⁇ 2 ).
  • the optimization engine may attempt to maximize a single value, which is now a function of these statistical moments.
  • This function may be referred to as a "utility function.”
  • the underlying overburden and reservoir models are generally complex and the uncertainty in these models is also generally complex and nonlinear.
  • the uncertainty in the model may therefore be represented as a plurality of realizations of the model in some embodiments. For example, one may be uncertain in the orientation of turbidite channels in a reservoir, and as such, multiple (N) realizations of the reservoir model may be generated, each with a likely channel orientation and geometry. The goal would be to have the collection of models reflect the possible spectrum of channel orientations.
  • a given control vector yielding a WPP may result in a different objective function value for each model realization.
  • the mean and standard deviation in the objective function value for this collection of models may be generated during optimization and used to compute f ⁇ , the risk corrected objective function value.
  • the objective function is generally evaluated N times during every trial, which may result in a significant computational overhead during uncertain optimizations, and further providing additional benefits when such computations are avoided as a result of feasibility evaluations that declare a WPP infeasible prior to computation of an objective function.
  • control variables that make up the control vector may be directly associated with target coordinates.
  • the easting and northing (X, Y) of the target locations may be considered.
  • a vertical well at this location may potentially penetrate the entire reservoir being considered.
  • a control vector may be defined including two control variables, one for X and one for Y.
  • M represents the number of targets
  • N 2M.
  • Each candidate target may have a feasible region (e.g., region 470) defined by an irregular polygon (or polygons).
  • a rotated bounding box 472 encloses the feasible region, and the axes of the bounding box correspond to the two control variables defining the target coordinate ( x j and y j ).
  • a mapping from the control variable coordinate system to the project geographic coordinate system is then a straightforward rotation, scaling and translation.
  • the next example is also dictated by target quality.
  • this example illustrates the optimization with a single platform and four deviated S-Wells (e.g., as shown in Fig. 7 ).
  • the number of targets (M) is 4 yielding 8 control variables to describe the targets, as in the vertical well example.
  • the tie point location for the platform may also be specified, thereby requiring an additional two control variables for total of 10.
  • a pattern driven strategy specifically a five spot pattern
  • discovering the optimal pattern parameters is generally more of an issue that identifying specific targets.
  • a basic five spot pattern may be optimized with as few as four control variables. This can also be made more complex if one allows for an asymmetric aspect ratio in the pattern, or deviated wells as in the previous example.
  • An illustration of an optimized five spot pattern is shown in Fig. 12 .
  • a framework for automated well placement planning as part of the field development planning workflow that in some embodiments may be performed quickly and using modest computing resources (e.g., performed in minutes using desktop hardware and software).
  • the framework in some embodiments automatically designs a well placement plan that optimizes an objective function (e.g., NPV or recovery) in the presence of subsurface uncertainty and operational risk tolerance.
  • a production forecast of the well placement plan may also be computed rigorously with an analytical or semi-analytical reservoir simulator. Engineering, financial, operational and geological constraints may also be incorporated into the computed plan.
  • the aforementioned methodology has many applications in the field of development planning context. For example, in some embodiments, multiple field development planning scenarios can be rapidly screened, and may be used in connection with selecting new wells, sidetracking existing wells and/or completing existing wells. In brownfields with hundreds of existing wells, infill locations can be quickly identified. Additional applications and uses of the herein-described techniques will be apparent to one of ordinary skill in the art having the benefit of the instant disclosure.

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Claims (23)

  1. Un procédé mis en oeuvre par ordinateur pour planification du positionnement de puits, le procédé consistant à :
    générer (410) un vecteur de commande comprenant une pluralité de variables de commande d'optimisation ;
    translater (420) le vecteur de commande à un plan de positionnement de puits candidats ;
    effectuer une première étude de faisabilité (428) pour le plan de positionnement de puits candidats par rapport à une ou plusieurs contraintes peu coûteuses ; et
    suite à la détermination de faisabilité du plan de positionnement de puits candidats d'après la première évaluation de faisabilité :
    calculer le résultat pour une fonction objectif basée sur le plan de positionnement de puits candidats à l'aide d'un simulateur de réservoir ; et
    effectuer une seconde évaluation de faisabilité pour le plan de positionnement de puits candidats en évaluant le résultat calculé pour la fonction objectif en fonction du plan de positionnement.
  2. Le procédé selon la revendication 1, comprenant en outre l'exécution d'une évaluation de faisabilité pour le vecteur de commande par rapport à une ou plusieurs contraintes linéaires avant la translation du vecteur de commande, dans lequel la translation du vecteur de commande n'est effectuée que suite à la détermination de faisabilité du vecteur de commande d'après la troisième étude de faisabilité..
  3. Le procédé selon la revendication 1, dans lequel le vecteur de commande comprend un vecteur initial de commande, et dans lequel le procédé comprend en outre la génération du vecteur initial de commande par translation d'un plan initial de positionnement de puits au vecteur initial de commande.
  4. Le procédé selon la revendication 1, consistant en outre, suite à la détermination d'infaisabilité du plan de positionnement de puits candidats d'après la première étude de faisabilité, à ignorer le calcul du résultat pour la fonction objectif et à effectuer la seconde évaluation de faisabilité.
  5. Le procédé selon la revendication 1, consistant en outre, suite à la détermination de faisabilité du plan de positionnement de puits candidats d'après la seconde étude de faisabilité, à déterminer que le plan de positionnement de puits candidats est un plan faisable de positionnement de puits
  6. Le procédé selon la revendication 1, consistant en outre, pour chacun de la pluralité des vecteurs de commande, à effectuer une opération de traitement d'essai connexe, dans lequel chaque opération de traitement d'essai consiste à :
    déterminer la faisabilité pour le vecteur de commande associé par rapport à une ou plusieurs contraintes linéaires ; et
    suite à la détermination de faisabilité du vecteur de commande associé par rapport à la ou les plusieurs contraintes linéaires ; et
    translater (420) le vecteur de commande associé à un plan associé de positionnement de puits
    candidats ;
    effectuer la première évaluation de faisabilité pour le plan associé de positionnement de puits candidats par rapport à la ou les plusieurs contraintes coûteuses ; et
    suite à la détermination de faisabilité du plan associé de positionnement de puits candidats d'après la première évaluation de faisabilité :
    calculer un résultat pour la fonction objectif sur la base du plan de positionnement de puits candidats à l'aide d'un simulateur de réservoir ; et
    effectuer la seconde évaluation de faisabilité pour le plan associé de positionnement de puits candidats en évaluant le résultat calculé pour la fonction objectif en fonction du plan associé de positionnement des puits candidats par rapport à la ou les plusieurs contraintes coûteuses.
  7. Le procédé selon la revendication 6, consistant en outre à générer au moins un de la pluralité des vecteurs de commande en extrapolant à partir d'un vecteur de commande préalable en fonction au moins en partie d'une évaluation de faisabilité effectuée pendant une opération de traitement d'essai pour le vecteur de commande préalable.
  8. Le procédé selon la revendication 7, dans lequel le vecteur de commande préalable est associé à un plan associé de positionnement des puits candidats déterminé comme étant infaisable, et dans lequel l'extrapolation à partir du vecteur de commande préalable est fondé sur un résultat d'au moins une évaluation de faisabilité effectuée durant l'opération de traitement d'essai pour le vecteur de commande préalable.
  9. Le procédé selon la revendication 7, consistant en outre à cesser la planification de positionnement de puits après exécution de l'opération de traitement d'essai pour chacun de la pluralité des vecteurs de commande suite à un état de cessation, dans lequel l'état de cessation est fondé sur une détermination qu'un nombre maximum d'opérations de traitement d'essai a été effectué, une détermination qu'une amélioration de la fonction objectif est au point mort, ou sur une combinaison de ces facteurs.
  10. Le procédé selon la revendication 1, dans lequel le simulateur de réservoir comprend un simulateur de réservoir analytique qui a accès à un modèle de simulation de réservoir à échelle grossière.
  11. Le procédé selon la revendication 10, consistant en outre à générer le modèle de simulation de réservoir à échelle grossière par mise à l'échelle supérieure d'un modèle géologique de réservoir à petite échelle.
  12. Le procédé selon la revendication 1, dans lequel la fonction objectif inclut un ou plusieurs des éléments suivants : valeur actualisée nette, retour sur investissement, rentabilité, indice de production, ou des combinaisons de ceux-ci.
  13. Le procédé selon la revendication 1, dans lequel le résultat de la fonction objectif comprend le calcul d'une pluralité de réalisations pour prendre en compte l'incertitude dans le modèle de réservoir, le procédé comprenant en outre l'optimisation d'une fonction d'utilité sur la base de la pluralité de résultats calculés pour la pluralité des réalisations.
  14. Le procédé selon la revendication 1, dans lequel la translation du vecteur de commande au plan de positionnement de puits candidats comprend l'identification d'une pluralité d'emplacements cibles dans un réservoir, la détermination d'une géométrie de complétion pour chaque emplacement cible et la détermination d'une trajectoire pour chaque emplacement cible.
  15. Le procédé selon la revendication 14, dans lequel la détermination de la géométrie de complétion pour un premier emplacement cible parmi la pluralité des emplacements cibles comprend la détermination d'au moins un emplacement de complétion sur la base d'une propriété d'une pluralité de cellules associées au premier emplacement cible et extraites d'un modèle de géologie de réservoir à petite échelle.
  16. Le procédé selon la revendication 15, dans lequel l'une ou les plusieurs contraintes peu coûteuses comprennent la faisabilité du premier emplacement cible en fonction d'une relation géométrique avec le modèle de réservoir à petite échelle, dans lequel la relation géométrique inclut une longueur de complétion minimale, une distance minimale par rapport à un contact des fluides, une distance minimale à une faille, ou une combinaison de ces éléments.
  17. Le procédé selon la revendication 15, dans lequel l'une ou les plusieurs contraintes peu coûteuses comprennent la faisabilité du premier emplacement cible en fonction d'une propriété du modèle de réservoir à petite échelle, dans lequel la propriété inclut la porosité minimale, la perméabilité minimale, la saturation en eau minimale, ou une combinaison de ces éléments
  18. Le procédé selon la revendication 1, dans lequel l'exécution de la première évaluation de faisabilité pour le plan de positionnement de puits candidats par rapport à la ou les plusieurs contraintes peu coûteuses comprend l'exécution d'une analyse anti-collision sur le plan de positionnement des puits candidats.
  19. Le procédé selon la revendication 1, dans lequel la ou les plusieurs contraintes peu coûteuses incluent un ou plusieurs des éléments suivants : mesure de la déviation angulaire du profil de puits, inclinaison maximale, portée maximale, nombre de plates-formes, nombre de puits, puits producteurs en débit, nombre de places, emplacement des plates-formes, séparation minimale des points de raccordement, espacement minimum de complétion, ou combinaison de ces éléments.
  20. Le procédé selon la revendication 1, dans lequel la ou les plusieurs contraintes coûteuses incluent un ou plusieurs puits subéconomiques, les puits producteurs en débit, ou une combinaison de ceux-ci.
  21. Le procédé selon la revendication 1, dans lequel le vecteur de commande comprend une ou plusieurs des coordonnées des emplacements cibles, coordonnées des points de raccordement, azimut d'un modèle, espacement des modèles, ou une combinaison de ceux-ci.
  22. Un appareil comprenant :
    au moins une unité de traitement ; et
    un code programme configuré sur exécution par l'au moins une unité de traitement pour exécuter la planification de positionnement de puits en :
    générant un vecteur de commande comprenant une pluralité des variables de commande d'optimisation ;
    translatant le vecteur de commande à un plan de positionnement de puits candidats ;
    effectuant la première évaluation de faisabilité pour le plan de positionnement de puits candidats par rapport à une ou plusieurs contraintes peu coûteuses ; et
    suite à la détermination de faisabilité du plan de positionnement de puits candidats d'après la première évaluation de faisabilité :
    calculant un résultat pour une fonction objectif basée sur le plan de positionnement de puits candidats à l'aide d'un simulateur de réservoir ; et
    effectuant une seconde évaluation de faisabilité pour le plan de positionnement de puits candidats en évaluant le résultat calculé pour la fonction objectif en fonction du plan de positionnement par rapport à une ou plusieurs contraintes coûteuses
  23. Un produit programme comprenant :
    un support lisible par ordinateur ; et
    un code programme stocké dans le support lisible par ordinateur et configuré sur exécution par au moins une unité de traitement pour exécuter la planification de positionnement de puits en :
    générant un vecteur de commande comprenant une pluralité de variables de commande d'optimisation ;
    translatant le vecteur de commande à un plan de positionnement de puits candidats ;
    effectuant une première évaluation de faisabilité pour le plan de positionnement de puits candidats par rapport à une ou plusieurs contraintes peu coûteuses ; et
    suite à la détermination de faisabilité du plan de positionnement de puits candidats d'après la première évaluation de faisabilité :
    calculant un résultat pour une fonction objectif basée sur le plan de positionnement de puits candidats à l'aide d'un simulateur de réservoir ; et
    effectuant une seconde évaluation de faisabilité pour le plan de positionnement de puits candidats en évaluant le résultat calculé pour la fonction objectif en fonction du plan de positionnement par rapport à une ou plusieurs contraintes coûteuses.
EP14743637.2A 2013-01-25 2014-01-24 Optimisation contrainte pour planification de positionnement de puits Active EP2948618B1 (fr)

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US14/162,687 US20140214387A1 (en) 2013-01-25 2014-01-23 Constrained optimization for well placement planning
PCT/US2014/013057 WO2014117030A1 (fr) 2013-01-25 2014-01-24 Optimisation contrainte pour planification de positionnement de puits

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