This invention is generally related to oil and gas wells, and more particularly to automatically computing preferred locations of wells and production platforms in an oil or gas field.
Determining the placement of wells is an important step in exploration and production management. Well placement affects the performance and viability of a field over its entire production life. However, determining optimum well placement, or even good well placement, is a complex problem. For example, the geology and geomechanics of subsurface conditions influence both drilling cost and where wells can be reliably placed. Well trajectories must also avoid those of existing wells. Further, wells have practical drilling and construction constraints. Constraints also exist at the surface, including but not limited to bathymetric and topographic constraints, legal constraints, and constraints related to existing facilities such as platforms and pipelines. Finally, financial uncertainty can affect the viability of different solutions over time.
There is a relatively long history of research activity associated with development of automated and semi-automated computation of field development plans (FDPs). Most or all studies recognize that this particular optimization problem is highly combinatorial and non-linear. Early work such as Rosenwald, G. W., Green, D. W., 1974, A Method for Determining the Optimum Location of Wells in a Reservoir Using Mixed-Integer Programming, Society of Petroleum Engineering Journal 14 (1), 44-54; and Beckner, B. L., Song, X., 1995, Field Development Planning Using Simulated Annealing, SPE 30650; and Santellani, G., Hansen, B., Herring, T., 1998, “Survival of the Fittest” an Optimized Well Location Algorithm for Reservoir Simulation, SPE 39754; and Ierapetritou, M. G., Floudas, C. A., Vasantharajan, S., Cullick, A. S., 1999, A Decomposition Based Approach for Optimal Location of Vertical Wells in American Institute of Chemical Engineering Journal 45 (4), pp. 844-859 is based on mixed-integer programming approaches. While this work is pioneering in the area, it principally focuses on vertical wells and relatively simplistic static models. More recently, work has been published on a Hybrid Genetic Algorithm (“HGA”) technique for calculation of FDPs that include non-conventional, i.e., non-vertical, wells and sidetracks. Examples of such work include Guiyaguler, B., Home, R. N., Rogers, L., 2000, Optimization of Well Placement in a Gulf of Mexico Waterflooding Project, SPE 63221; and Yeten, B., Durlofsky, L. J., Aziz, K., 2002, Optimization of Nonconventional Well Type, Location and Trajectory, SPE 77565; and Badra, O., Kabir, C. C., 2003, Well Placement Optimization in Field Development, SPE 84191; and Guiyaguler, B., Home, R. N., 2004, Uncertainty Assessment of Well Placement Optimization, SPE 87663. While the HGA technique is relatively efficient, the underlying well model is still relatively simplistic, e.g., one vertical segment down to a kick-off depth (heal), then an optional deviated segment extending to the toe. The sophistication of optimized FDPs based on the HGA described above has grown in the past few years as the time component is being included to support injectors, and uncertainty in the reservoir model is being considered. Examples include Cullick, A. S., Heath, D., Narayanan, K., April, J., Kelly, J., 2003, Optimizing multiple-field scheduling and production strategy with reduced risk, SPE 84239; and Cullick, A. S., Narayanan, K., Gorell, S., 2005, Optimal Field Development Planning of Well Locations With Reservoir Uncertainty, SPE 96986. However, improved automated calculation of FDPs remains desirable.
An automated process for determining the surface and subsurface locations of producing and injecting wells in a field is disclosed. The process involves planning multiple independent sets of wells on a static reservoir model using an automated well planner. The most promising sets of wells are then enhanced with dynamic flow simulation using a cost function, e.g., maximizing either recovery or economic benefit. The process is characterized by a hierarchical workflow which begins with a large population of candidate targets and drain holes operated upon by simple (fast) algorithms, working toward a smaller population operated upon by complex (slower) algorithms. In particular, as the candidate population is reduced in number, more complex and computationally intensive algorithms are utilized. Increasing algorithm complexity as candidate population is reduced tends to produce a solution in less time, without significantly compromising the accuracy of the more complex algorithms.
In accordance with one embodiment of the invention, a method of calculating a development plan for at least a portion of a field containing a subterranean resource, comprises the steps of: identifying a population of target sets in the field; reducing this population by selecting a first sub population with a first analysis tool; reducing the first sub population by selecting a second sub population of target sets with a second analysis tool, the second tool utilizing greater analysis complexity than the first analysis tool; calculating FDPs from the second sub population of target sets; and presenting the FDPs in tangible form.
In accordance with another embodiment of the invention, a computer-readable medium encoded with a computer program for calculating a development plan for at least a portion of a field containing a subterranean resource, comprises: a routine which identifies a population of target sets in the field; a routine which reduces the population of target sets by selecting a first sub population of the target sets with a first analysis tool; a routine which reduces the first sub population by selecting a second sub population of target sets with a second analysis tool, the second tool utilizing greater analysis complexity than the first analysis tool; a routine which calculates a FDP from the second sub population of target sets; and a routine which presents the FDPs in tangible form.
Further features and advantages of the invention will become more readily apparent from the following detailed description when taken in conjunction with the accompanying drawings.
The target selection operation (100) is initialized by generating a large initial population (112) of target sets from a geological model (110). For example, 1000 different target sets might be generated, although the actual population size is dependent on the complexity of the field and other considerations. Each member of the population is a complete set of targets to drain the reservoir(s), and each target is characterized by an estimate of its value. For example, a simple value estimate is the associated stock tank oil initially in place (“STOIIP”). In subsequent operations, the large initial population of target sets is gradually reduced in size as each step progressively identifies the more economically viable subsets of the population.
The drain hole selection operation (102) includes generating a population (114) of drain-hole sets from the target population (112). Each drain hole is an ordered set of targets that constitutes the reservoir-level control points in a well trajectory. Each member of the generated population (114) is a complete set of drain holes to drain the reservoir(s). Each drain hole set comprises targets from a single target set created in the previous operation. It should be noted that multiple drain hole sets may be created for a single target set. Each drain hole set has an associated value which could be, for example and without limitation, STOIIP, initial flow rate, decline curve profile, or material balance profile.
The reservoir trajectory selection operation (104) includes generating a population (116) of trajectory sets from the drain hole population (114). In particular, each member of the generated population (116) represents a completion derived from the corresponding drain-hole set created in the previous operation (102). Each well trajectory is a continuous curve connecting the targets in a drain hole. At the end of this operation (104), the approximate economic value of each trajectory set is evaluated based on the STOIIP values of its targets and the geometry of each well trajectory. These values are used to reduce the size of the population by selecting the population subset with the largest economic values, i.e., the “fittest” individuals. For example, by selecting the “fittest” 10% of individual subsets, the size of the population can be reduced by one order of magnitude, e.g., from 1000 to 100.
In the overburden trajectory selection operation (106) each trajectory in the remaining population (116) of trajectory sets created in the previous operation (104) is possibly modified to account for overburden effects such as drilling hazards. At the end of this operation (106) the approximate economic value of each trajectory set is evaluated using STOIIP and geometry, as in the previous operation, but also with respect to drilling hazards. The “fittest” individuals with respect to economic value are then selected and organized into a population (118) for use in the next operation (108). For example, by selecting the “fittest” 10% of these individuals it is possible to further reduce the size of the population by another order of magnitude, e.g., from 100 to 10.
The FDP selection operation (108) includes performing rigorous reservoir simulations on the remaining relatively small population (118) of trajectory sets, e.g., 10. The economic value of each member of the population is evaluated using trajectory geometry, drilling hazards and the production predictions of the reservoir simulator. These values can be used to rank the FDPs in the remaining small population. The FDP with the greatest rank may be presented as the selected plan, or a set of greatest ranked plans may be presented to permit planners to take into account factors not included in the automated computations, e.g., political constraints. The result is a FDP population (120).
A particular embodiment of the workflow of
An embodiment of drain hole selection is illustrated in greater detail in
An embodiment of reservoir trajectory selection is illustrated in greater detail by
An embodiment of overburden trajectory selection is illustrated in greater detail by
FDP Selection is performed on the relatively small TJSP produced from the previous step. The operation includes rigorous reservoir simulations. As illustrated by step (806), for each TJS in TJSP, a full reservoir simulation is performed. The financial value of the reservoir production streams, possibly expressed as a net present value (NPV)NPV, may be utilized to rank members of the TJSP. As shown in step (808), results are then presented in tangible form, such as printed, on a monitor, and recorded on computer readable media. For example, the member with the greatest NPV and the ranking may be presented.
Referring now to
The embodiments outlined above operate on a single “certain” geological, geomechanical and facilities model. Modem modeling tools such as Petrel 2007 allow “uncertain” earth models to be generated. The invention described here could be implemented within this context so that an “uncertain” FDP would be generated. An uncertain earth model is typically described through multiple realizations of certain earth models. As such, an embodiment of an uncertain FDP would be through multiple realizations.
It is important to recognize that because of unknown and incalculable factors, the most successful, robust and efficient realization may differ from the results of the computation. Further, it is important to note that different problems may demand different realizations of the algorithm.
While the invention is described through the above exemplary embodiments, it will be understood by those of ordinary skill in the art that modification to and variation of the illustrated embodiments may be made without departing from the inventive concepts herein disclosed. Moreover, while the preferred embodiments are described in connection with various illustrative structures, one skilled in the art will recognize that the system may be embodied using a variety of specific structures. Accordingly, the invention should not be viewed as limited except by the scope and spirit of the appended claims.
Number | Name | Date | Kind |
---|---|---|---|
4249776 | Shuck et al. | Feb 1981 | A |
6549879 | Cullick et al. | Apr 2003 | B1 |
7054753 | Williams et al. | May 2006 | B1 |
7062420 | Poe, Jr. | Jun 2006 | B2 |
7460957 | Prange et al. | Dec 2008 | B2 |
20040153299 | Colvin et al. | Aug 2004 | A1 |
20060151214 | Prange et al. | Jul 2006 | A1 |
20070156377 | Gurpinar et al. | Jul 2007 | A1 |
20080288226 | Gurpinar et al. | Nov 2008 | A1 |
Number | Date | Country | |
---|---|---|---|
20080300793 A1 | Dec 2008 | US |