METHOD AND SYSTEM FOR SELECTING LOST CIRCULATION MATERIALS FOR BORING A WELL

Abstract

A method for selecting lost circulation materials (LCM) for boring a well involves determining, for a multitude of LCM blends, a multitude of overall scores based on characteristics associated with each of the multitude of LCM blends, and establishing a list of LCM blend candidates. Establishing the list of LCM blend candidates involves including a first of the multitude of LCM blends in a list of LCM blend candidates based on a first overall score associated with the first LCM blend, and excluding a second of the multitude of LCM blends from the list of LCM blend candidates, based on a second overall score associated with the second LCM blend. The first overall score is greater than the second overall score.

Description

BACKGROUND

Drilling mud is used when boring a well. It is pumped from the surface to the bottom of the wellbore through joined pipes and then circulated back to the surface. One of the major challenges in drilling operations is lost circulation of drilling mud where a large portion or all of the drilling mud is lost into the formation instead of circulating back up to the surface. There are many possible reasons for this phenomenon such as the high permeability of the formation, the existence of natural fractures in the formation, and cracks caused by the high wellbore pressure. To mitigate mud losses, drilling engineers have been putting lost circulation materials (LCMs) into the drilling mud. The LCM particles travel with the drilling mud into the pores and fractures in the formation, shoring it up and blocking at least some of the fluid from flowing into the formation. There are many LCM products of different size distribution, elastic properties, and strength properties. Accordingly, methods and systems that perform the selection of one or more LCM products would be beneficial.

SUMMARY

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

In general, in one aspect, embodiments relate to a method for selecting lost circulation materials (LCM) for boring a well, the method comprising: determining, for a plurality of LCM blends, a plurality of overall scores based on characteristics associated with each of the plurality of LCM blends; and establishing a list of LCM blend candidates comprising: including a first of the plurality of LCM blends in a list of LCM blend candidates based on a first overall score associated with the first LCM blend; and excluding a second of the plurality of LCM blends from the list of LCM blend candidates, based on a second overall score associated with the second LCM blend, wherein the first overall score is greater than the second overall score.

In general, in one aspect, embodiments relate to a system for selecting lost circulation materials (LCM) for boring a well, the system comprising: a computer system executing an LCM blend optimization engine that: determines, for a plurality of LCM blends, a plurality of overall scores based on characteristics associated with each of the plurality of LCM blends; and establishes a list of LCM blend candidates comprising: including a first of the plurality of LCM blends in a list of LCM blend candidates based on a first overall score associated with the first LCM blend; and excluding a second of the plurality of LCM blends from the list of LCM blend candidates, based on a second overall score associated with the second LCM blend, wherein the first overall score is greater than the second overall score.

In general, in one aspect, embodiments relate to a non-transitory machine-readable medium comprising a plurality of machine-readable instructions executed by one or more processors, the plurality of machine-readable instructions causing the one or more processors to perform operations comprising: determining, for a plurality of LCM blends, a plurality of overall scores based on characteristics associated with each of the plurality of LCM blends; and establishing a list of LCM blend candidates comprising: including a first of the plurality of LCM blends in a list of LCM blend candidates based on a first overall score associated with the first LCM blend; and excluding a second of the plurality of LCM blends from the list of LCM blend candidates, based on a second overall score associated with the second LCM blend, wherein the first overall score is greater than the second overall score.

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

BRIEF DESCRIPTION OF DRAWINGS

Specific embodiments of the disclosed technology will now be described in detail with reference to the accompanying figures. Like elements in the various figures are denoted by like reference numerals for consistency.

FIG. 1 shows a drilling operation in accordance with one or more embodiments.

FIG. 2 shows a system in accordance with one or more embodiments.

FIG. 3 shows a flowchart of a method in accordance with one or more embodiments.

FIG. 4 shows a flowchart of a method in accordance with one or more embodiments.

FIG. 5 shows a load decomposition in accordance with one or more embodiments.

FIG. 6 shows a visualization of example LCM blends in accordance with one or more embodiments.

FIG. 7 shows a computer system in accordance with one or more embodiments.

DETAILED DESCRIPTION

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

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

In general, embodiments of the disclosure include systems and methods for selecting lost circulation materials for boring a well. Drilling mud is used when boring a well. It is pumped from the surface to the bottom of the wellbore through joined pipes and then circulated back to the surface. One of the major challenges in drilling operations is lost circulation of drilling mud where a large portion or all of the drilling mud is lost into the formation instead of circulating back up to the surface. There are many possible reasons for this phenomenon such as the high permeability of the formation, the existence of natural fractures in the formation, cracks caused by the high wellbore pressure, etc. To mitigate mud losses, drilling engineers have been putting lost circulation materials (LCMs) into the drilling mud. The LCM particles travel with the drilling mud into the pores and fractures in the formation, shoring it up and blocking at least some of the fluid from flowing into the formation. There are many LCM products of different size distribution, elastic properties, and strength properties. Engineers often blend several LCM products together to design the most suitable blend. In one or more embodiments, the most suitable LCM blend is selected without requiring manual input of the LCM products used in the blend. Embodiments of the disclosure automatically evaluate different combinations of LCM blends (including different volumes or mass fractions of each LCM component) and lists the most suitable combinations along with their scores. An LCM blend, to be used for boring the well may then be selected from that list.

Embodiments of the disclosure, thus, reduce the effort associated with conventionally determining the most suitable blend of LCMs, which may require numerous repetitions of manually determining blends based on the experience of an engineer, where it is not unlikely that, when performing the manual selection, the best combination is missed.

FIG. 1 shows a drilling system (100) that may include a top drive drilling rig (110) arranged around the setup of a drill bit logging tool (120). A top drive drilling rig (110) may include a top drive (111) that may be suspended in a derrick (112) by a travelling block (113). In the center of the top drive (111), a drive shaft (114) may be coupled to a top pipe of a drill string (115), for example, by threads. The top drive (111) may rotate the drive shaft (114), so that the drill string (115) and a drill bit logging tool (120) cut the rock at the bottom of a wellbore (116). Drilling mud may be pumped into the wellbore (116) through a mud line (119), the drive shaft (114), and/or the drill string (115).

The control system (144) may include one or more programmable logic controllers (PLCs) that include hardware and/or software with functionality to control one or more processes performed by the drilling system (100). Specifically, a programmable logic controller may control valve states, fluid levels, pipe pressures, warning alarms, and/or pressure releases throughout a drilling rig. For example, the control system (144) may be coupled to the sensor assembly (123) in order to perform various program functions for up-down steering and left-right steering of the drill bit (124) through the wellbore (116). The control system (144) may further control the pumping of the drilling mud. While one control system is shown in FIG. 1, the drilling system (100) may include multiple control systems for managing various well drilling operations, maintenance operations, well completion operations, and/or well intervention operations. The control system (144) may be based on a computer system as shown in FIG. 6.

The wellbore (116) may include a bored hole that extends from the surface into a target zone of the hydrocarbon-bearing formation, such as the reservoir. An upper end of the wellbore (116), terminating at or near the surface, may be referred to as the “up-hole” end of the wellbore (116), and a lower end of the wellbore, terminating in the hydrocarbon-bearing formation, may be referred to as the “down-hole” end of the wellbore (116). The wellbore (116) may facilitate the circulation of drilling mud during well drilling operations, the flow of hydrocarbon production (“production”) (e.g., oil and gas) from the reservoir to the surface during production operations, the injection of substances (e.g., water) into the hydrocarbon-bearing formation or the reservoir during injection operations, or the communication of monitoring devices (e.g., logging tools) into the hydrocarbon-bearing formation or the reservoir during monitoring operations (e.g., during in situ logging operations).

While FIG. 1 shows various configurations of components, other configurations may be used without departing from the scope of the disclosure. For example, various components in FIG. 1 may be combined to create a single component. As another example, the functionality performed by a single component may be performed by two or more components. Further, while not illustrated in FIG. 1, the formation may have a high permeability, natural fractures and/or cracks caused by high wellbore pressure, which may result in a lost circulation of drilling mud during the drilling.

FIG. 2 shows a system (200) for selecting lost circulation materials (LCMs) for boring a wall, in accordance with one or more embodiments. The system (200) includes an LCM blend optimization engine (220) that operates on data obtained from the database (210) to output a list of LCM blend candidates (230). The LCM blend optimization engine (220) may execute on a computing system as shown in FIG. 7. The database (210) may store various data associated with the boring of the well, such as in-situ stress data (211), pore pressure data (212), physical and mechanical properties of the rock (213), the wellbore geometry (214), and the mud weight (215). Some of these data may be entered by a user, some of them may be measured by equipment, and some of them may be calculated. A discussion of these data is provided below. In one or more embodiments, the list of LCM blend candidates (230) is generated by an optimization algorithm (222) of the LCM blend optimization engine (220). Each of these components is subsequently described in reference to the flowcharts of FIGS. 3 and 4, followed by an example.

FIGS. 3 and 4 show flowcharts in accordance with one or more embodiments. FIG. 3 shows a method for selecting LCMs for boring a well, and FIG. 4 shows a method for establishing a list of LCM blend candidates, in accordance with one or more embodiments.

Execution of one or more steps in FIGS. 3 and 4 may involve one or more components of the systems as described in FIGS. 1 and 2. For example, the method(s) may be executed on a system (200) or on any other computer system, e.g., as shown in FIG. 6.

While the various blocks in FIGS. 3 and 4 are presented and described sequentially, one of ordinary skill in the art will appreciate that some or all of the blocks may be executed in different orders, may be combined or omitted, and some or all of the blocks may be executed in parallel. Furthermore, the blocks may be performed actively or passively.

FIG. 3 shows a method (300) for selecting LCMs for boring a well.

In Step 310, borehole, mud and formation properties are gathered. The gathering of these properties involves the subsequently described steps.

In Step 312, in-situ stresses and pore pressure are determined. These properties may be obtained from well logs, leak-off tests, and measurements from offset wells and may be stored in a database as shown in FIG. 2. An additional discussion is provided in reference to FIG. 5.

In Step 314, the rock's physical and mechanical properties are obtained. The properties may include tensile strength and fracture toughness, e.g., obtained from well logs and core samples and may be stored in a database as shown in FIG. 2.

In Step 316, the wellbore geometry is determined. The determined parameters may include the borehole diameter, inclination angle and/or azimuth. The parameters may be obtained from a well survey log or from the well trajectory design and may be stored in a database as shown in FIG. 2.

In Step 318, the mud weight of the mud used for the drilling operation is obtained and may be stored in a database as shown in FIG. 2.

In Step 320, a list of LCM candidates is established. A description is provided in reference to FIG. 4

In Step 330, an LCM blend is selected from the list. The selection may be performed as described below in reference to FIG. 6.

In Step 340, the selected LCM blend is applied for boring the well. In other words, the LCM blend is injected into the drilling mud circulation and is expected to reduce or eliminate the issue with lost circulation of drilling mud.

FIG. 4 shows a method (400) for establishing a list of LCM blend candidates. In one or more embodiments, the list of LCM blends may be determined based on an optimization. The list may include highest-ranked LCM blends, based on a total score. Any optimization may be used, as further discussed below.

In Step 402, an LCM blend is selected for performing the subsequently described analysis. The selected LCM blend may include certain LCM components at certain concentrations (volume or mass fraction). The LCM components and/or their concentrations selected in Step 402 may be determined by the optimization. The optimization may systematically select LCM components and/or their concentrations based on assumptions made by the optimization. Different such optimizations are discussed below.

In Step 404, a stress cage improvement is determined for the selected LCM blend. The theory behind stress cage wellbore strengthening associated with drilling mud is depending on the following points: Small fractures are allowed to form in the wellbore wall. They may be held open using bridging particles near the fracture opening. The bridging particles may be selected to establish a low permeability to provide pressure isolation. This may create an increased hoop stress around the wellbore (a “stress cage” effect), provided the induced fracture is bridged at, or close to, the wellbore wall.

Rock tensile strength TS (psi) determines the fracture initiation limit of the wellbore wall following Terzaghi criterion. After LCM particles have been deposited inside a fracture, the effective minimum principal stress around the wellbore is assumed to become [σ_min^post(R, θ)−p_i] (psi), where σ_min^post (R, θ) is the mechanical minimum stress tangential to a fractured wellbore wall at angle θ and wellbore radius R, p_i is pore pressure at the partially permeable wall. The larger [σ_min^post(R, θ)−p_i], the better, with compression positive convention. The normalized stress cage improvement metric C_SC may be defined as follows.

$\begin{array}{cc}{C}_{\mathrm{SC}}=\frac{{\sigma }_{\min }^{\mathrm{post}}\left(r_w,\theta \right)-p_i-\mathrm{TS}}{p_i}& \left(1\right)\end{array}$

Additional details, including a description of the computation of σ_min^post (R, θ) are provided below in reference to FIG. 5. Computation of σ_min^post (R, θ) may be based on any suitable model without departing from the disclosure.

In Step 406, a fracture tip stress intensity factor improvement is determined for the selected LCM blend. Suppose the critical stress intensity factor (a.k.a. the fracture toughness) of the formation rock is K_IC (psi.in^0.5). After using the given LCM composition, the fracture tip stress intensity factor is K_I^post (psi.in^0.5). The smaller K_I^Post, the better. The normalized K_I improvement metric may be defined as follows.

$\begin{array}{cc}{C}_{\mathrm{KI}}=\frac{K_{\mathrm{IC}}-K_I^{\mathrm{post}}}{K_{\mathrm{IC}}}& \left(2\right)\end{array}$

Additional details, including a description of the computation of K_I^post are provided below in reference to FIG. 5. Computation of K_I^post may be based on any suitable model without departing from the disclosure.

In Step 408, the financial cost is determined for the selected LCM blend. Suppose the LCM blend is composed of n products P₁, . . . , P_n with the corresponding mass fraction f₁, . . . , f_n and per-pound costs C₁, . . . , C_n. The per-pound cost for the LCM blend is then

$\begin{array}{cc}{C}_{\mathrm{USD}}=\sum _{i=1}^n{f}_i{C}_i& \left(3\right)\end{array}$

In Step 410, an overall score is determined, based on stress cage improvement, tip stress intensity factor improvement, and financial cost. The overall score may be defined as follows defined as follows.

$\begin{array}{cc}C=w_{\mathrm{SC}}{C}_{\mathrm{SC}}+w_{\mathrm{KI}}{C}_{\mathrm{KI}}-w_{\mathrm{USD}}{C}_{\mathrm{USD}}& \left(4\right)\end{array}$

where w_SC, w_KI, w_USD are the non-negative weights of the three metrics C_SC, C_KI, and C_USD, respectively. A higher C is considered better. By default, the weights may be set to 1, signaling equal importance of the three metrics. If a metrics is not at all important, its weight may be set to 0. Note that other metrics, e.g., viscosity improvement to the drilling mud, etc. may also be added as desired.

After completion of Step 410, an overall score for the LCM blend, selected in Step 402, is available. The execution of the method (400) may now continue by repeating steps 402-410 for a different LCM blend. The steps may be repeated for any LCM blends to be evaluated. Alternatively, the steps of the method (400) may be performed in parallel for multiple LCM blends. The number of candidate LCM blends may be significant. For example, assuming that there are 100 LCM products from which a combination of four are to be mixed, there would be a total of 3,921,225 different choices. In order to identify the most promising LCM blends (i.e., those with a highest overall score) while limiting the number of iterations through the method (400), the selection of LCM blends may be driven by an optimization. Any type of optimization may be used, but since the number of combinations of LCMs may be very large, swarm optimization algorithms such as particle swarm optimization, genetic algorithm, ant colony optimization, and simulated annealing algorithm may be particularly appropriate choices. Alternatively, machine learning techniques such as reinforcement learning could also be used. These methods may provide an optimal solution that maximizes the overall score C. The objectives C_SC, C_KI, and C_USD may be conflicting, i.e., an improvement in one objective leads to a degradation in another one. Therefore, it may be desirable to obtain a list of solution candidates for the user to pick from. To do this, Pareto front search algorithms may be used.

FIG. 5 shows a load decomposition (500), in accordance with one or more embodiments. The load decomposition (500) and the associated subsequent discussions provide an example of how σ_min^post and K_I^post, as used in Steps 404 and 406 of the method (400), may be determined.

The load decomposition scheme (400) may be used to obtain the stress distribution around and about the wellbore. In this scheme, the problem is decomposed to the following independent subproblems for finding the associated stress components:

• • In-plane stress problem, (σ_rr, σ_θθ),
  • Anti-plane shear stress problem, (σ_rz, σ_θz), and
  • Uniaxial stress problem, σ_zz.

In-plane in this context refers to the plane perpendicular to the wellbore axis. Representing the fracture as distribution of edge dislocations b_y (ξ) and expressing the stress {circumflex over (σ)}_αβ(r, θ; ξ) along the fracture,

$\begin{array}{cc}{\sigma }_{\alpha \beta }\left(r,\theta ;b_y\right)={\sigma }_{\mathrm{\alpha \beta }}^{\mathrm{Kr}}\left(r,\theta \right)+\frac{G}{2\pi \left(1-v\right)}{\int }_R^{R+L}{b}_y\left(\xi \right)\left[{\hat{\sigma }}_{\mathrm{\alpha \beta }}\left(r;\theta ;\xi \right)+{\hat{\sigma }}_{\mathrm{\alpha \beta }}\left(r,\theta -\pi ;\xi \right)\right]d\xi & \left(5\right)\end{array}$

where σ_αβ^Kr(r, θ) is the stress of a nonfracture wellbore. The hoop component of this stress can be written in terms of the far field stresses σ_H, σ_h as well as the wellbore pressure pw as:

$\begin{array}{cc}{\sigma }_{\mathrm{\theta \theta }}^e\left(r,\theta \right)=\frac{{\sigma }_H+{\sigma }_h}{2}\left(1+\frac{R^2}{r^2}\right)-\frac{{\sigma }_H-{\sigma }_h}{2}\left(1+3\frac{R^4}{r^4}\right)\cos \left(2\theta \right)+P_w\frac{R^2}{r^2}& \left(6\right)\end{array}$

where R is the wellbore radius, σ_H is the maximum horizontal stress, and σ_h is the minimum horizontal stress. The hoop stress component induced by Mode I fracture {circumflex over (σ)}_θθ(r, θ=0; ξ) is expressed by:

$\begin{array}{cc}{\hat{\sigma }}_{\mathrm{\theta \theta }}\left(r,\theta =0;\xi \right)=\frac{1}{r-\xi }-\frac{\xi }{r\xi -R^2}-\frac{\xi \left({\xi }^2-R^2\right)}{{\left(r\xi -R^2\right)}^2}+\frac{{R^2\left({\xi }^2-R^2\right)}^2}{{\xi \left(r\xi -R^2\right)}^3}+\frac{{\xi }^2-R^2}{r^2\xi }& \left(7\right)\end{array}$

To simulate the gradual closure of fracture onto LCM particles, the incremental LCM strain at radial distance r can be formulated in the following form:

$\begin{array}{cc}\delta p_i+{\sigma }_{\mathrm{\theta \theta }}\left(r,0;b_{y,i}\right)-{\sigma }_{\mathrm{\theta \theta }}\left(r,0;b_{y,i-1}\right)=-E_{\mathrm{LCM}}\left[{\epsilon }_i\left(r\right)\right]\delta {\epsilon }_i\left(r\right)& \left(8\right)\end{array}$

where δp_i=p_i−p_i−1 is the incremental load due to leakoff. b_y^pre=b_y,0 and b_y^post=b_y,N pertain to the pre-leakoff and post-leakoff states of the fracture. The LCM incremental strain may be written in terms of fracture widths at steps i and (i−1) as δε(r)=1−w_i(r)/w_i−1(r), and the total strain is ¿_i(r)=Σ_k=1ⁱ δε_k(r). E_LCM(ε_i) is the strain-dependent LCM modulus which is measured in the lab.

Once b_post^y is obtained from Equation (8), the fracture tip stress intensity factor can be calculated as

$\begin{array}{cc}{K}_I^{\mathrm{post}}=\frac{G}{2\left(1-v\right)}\underset{r\to {\left(R+L\right)}^{-}}{\lim }\left[\sqrt{2\pi \left(L+R-r\right)}{b}_y^{\mathrm{post}}\left(r\right)\right]& \left(9\right)\end{array}$

where L is the drilling-induced fracture length, usually set to 6 inches, G is the shear modulus, and v is the Poisson's ratio of the rock.

The minimum principal stress is calculated from stress components on wellbore wall tangential plane by Equation (5), as follows

$\begin{array}{cc}{\sigma }_{\min }^{\mathrm{post}}=\frac{{\sigma }_{\mathrm{zz}}+{\sigma }_{\mathrm{\theta \theta }}}{2}-\sqrt{{\left(\frac{{\sigma }_{\mathrm{zz}}-{\sigma }_{\mathrm{\theta \theta }}}{2}\right)}^2+{\sigma }_{\mathrm{\theta z}}^2}& \left(10\right)\end{array}$

Tables 1 and 2 are based on an example in accordance with embodiments of the disclosure. The example is for an inclined wellbore. The formation wellbore and drilling mud data are listed in Table 1. The list of LCM blend candidates is provided in Table 2.

TABLE 1
Formation, wellbore, and drilling mud data.
Parameters	Value	Unit
True vertical depth	16000	ft
Wellbore diameter	12.25	inch
Wellbore inclination angle	40	degree
Wellbore azimuth	15	degree
Fracture length	6	inch
Reservoir radius	1000	ft
Overburden stress gradient	1	psi/ft
Maximum horizontal stress gradient	0.8	psi/ft
Maximum horizontal stress azimuth	20	degree
Minimum horizontal stress gradient	0.65	psi/ft
Pore pressure gradient	0.45	psi/ft
Young's modulus	1.92	Mpsi
Poisson's ratio	0.25	unitless
Formation permeability	0.5	md
Tensile strength	0	psi
Critical tip stress intensity factor	10000	psi · in0.5
Mud weight	13.9	ppg
Filter cake thickness	2/32	inch
Drilling mud solids content by volume	14.5	%
API HTHP filtration volume	16	cc
API LTLP filtration volume	8	cc
HTHP spurt loss	0	cc
LTLP spurt loss	0	cc

TABLE 2
LCM data. D10, D50, and D90 are statistical parameters
indicating the size below which 10%, 50%, and 90%,
respectively, of all particles are found.
	Particle Size	Strain (unitless) at
	Distribution	1, 5, 10, 15 ksi in	Normalized
LCM	D10 (μm),	Confined	Price Per
Product	D50 (μm), D90 (μm)	Compression Test	Pound
LCM1	15, 55, 125	0.28, 0.46, 0.53, 0.57	0.75
LCM2	224, 460, 814	0.25, 0.44, 0.53, 0.58	0.8
LCM3	183, 387, 633	0.28, 0.47, 0.52, 0.55	0.65
LCM4	343, 548, 832	0.28, 0.46, 0.53, 0.55	0.5
LCM5	15, 28, 50	0.28, 0.46, 0.52, 0.55	0.6
LCM6	25, 69, 129	0.04, 0.12, 0.2, 0.26	0.7
LCM7	50, 191, 319	0.06, 0.13, 0.21, 0.27	0.85
LCM8	162, 334, 459	0.06, 0.14, 0.21, 0.27	1
LCM9	383, 603, 1005	0.20, 0.38, 0.46, 0.51	0.9
LCM10	711, 1078, 1533	0.11, 0.28, 0.38, 0.44	0.95

FIG. 6 shows a visualization of example LCM blend candidates in accordance with embodiments of the disclosure. Each circle represents an LCM blend. The numbers inside the circles are the normalized price per pound of the LCM blends. The size of the circle corresponds to the normalized price per pound.

Using the Vicker's criterion for LCM particle size distribution and the poroelastic solution for wellbore's effective stresses, the optimal mass composition of each LCM combination may be computed, along with the scores. For example, for the LCM combination of LCM3, LCM6, and LCM8, the optimal mass composition is 43.7% LCM3, 21.7% LCM6, and 34.6% LCM8. The normalized stress cage improvement is 0.35, the normalized fracture tip stress intensity factor improvement is 0.51, and the normalized price per pound of the optimal blend is 0.78. Note that any LCM particle size distribution criteria, e.g., Vicker's criterion, one-third rule, etc., can be used. Likewise, any solutions for wellbore's effective stresses, such as elastic, thermoelastic, viscoelastic, poroelastic, porothermoelastic, poroviscoelastic, etc., can be used. Applying the Pareto front search algorithm, we obtain the list of LCM blend candidates. FIG. 6, discussed below, shows 12 such LCM blend candidates.

As the example (600) in FIG. 6 illustrates, there are trade-offs among the LCM blend candidates. An LCM blend that maximizes stress cage improvement may have a low score for fracture tip stress intensity factor improvement, and vice versa. Adding the price factor, the relationships among the LCM blend candidates may become more non-linear. In the example (600), if all three factors are weighted equally, then the optimal solution is identified by the center circle that has the normalized price per pound of 0.58. This solution minimizes the price while balancing the other two factors. By inspection of a plot such as the one shown in FIG. 5, the engineers in the field can make an informed decision about what LCM blend to use. For example, if the priority is to minimize fracture propagation, engineers may put more weight on the fracture tip stress intensity factor improvement. If the priority is to widen the mud weight window, engineers may put more weight on the stress cage improvement.

Embodiments may be implemented on a computer system. FIG. 7 is a block diagram of a computer system (702) used to provide computational functionalities associated with described algorithms, methods, functions, processes, flows, and procedures as described in the instant disclosure, according to an implementation. The illustrated computer (702) is intended to encompass any computing device such as a high performance computing (HPC) device, a server, desktop computer, laptop/notebook computer, wireless data port, smart phone, personal data assistant (PDA), tablet computing device, one or more processors within these devices, or any other suitable processing device, including both physical or virtual instances (or both) of the computing device. Additionally, the computer (702) may include a computer that includes an input device, such as a keypad, keyboard, touch screen, or other device that can accept user information, and an output device that conveys information associated with the operation of the computer (702), including digital data, visual, or audio information (or a combination of information), or a GUI.

The computer (702) can serve in a role as a client, network component, a server, a database or other persistency, or any other component (or a combination of roles) of a computer system for performing the subject matter described in the instant disclosure. The illustrated computer (702) is communicably coupled with a network (730). In some implementations, one or more components of the computer (702) may be configured to operate within environments, including cloud-computing-based, local, global, or other environment (or a combination of environments).

At a high level, the computer (702) is an electronic computing device operable to receive, transmit, process, store, or manage data and information associated with the described subject matter. According to some implementations, the computer (702) may also include or be communicably coupled with an application server, e-mail server, web server, caching server, streaming data server, business intelligence (BI) server, or other server (or a combination of servers).

The computer (702) can receive requests over network (730) from a client application (for example, executing on another computer (702)) and responding to the received requests by processing the said requests in an appropriate software application. In addition, requests may also be sent to the computer (702) from internal users (for example, from a command console or by other appropriate access method), external or third-parties, other automated applications, as well as any other appropriate entities, individuals, systems, or computers.

Each of the components of the computer (702) can communicate using a system bus (703). In some implementations, any or all of the components of the computer (702), both hardware or software (or a combination of hardware and software), may interface with each other or the interface (704) (or a combination of both) over the system bus (703) using an application programming interface (API) (712) or a service layer (713) (or a combination of the API (712) and service layer (713). The API (712) may include specifications for routines, data structures, and object classes. The API (712) may be either computer-language independent or dependent and refer to a complete interface, a single function, or even a set of APIs. The service layer (713) provides software services to the computer (702) or other components (whether or not illustrated) that are communicably coupled to the computer (702). The functionality of the computer (702) may be accessible for all service consumers using this service layer. Software services, such as those provided by the service layer (713), provide reusable, defined business functionalities through a defined interface. For example, the interface may be software written in JAVA, C++, or other suitable language providing data in extensible markup language (XML) format or other suitable format. While illustrated as an integrated component of the computer (702), alternative implementations may illustrate the API (712) or the service layer (713) as stand-alone components in relation to other components of the computer (702) or other components (whether or not illustrated) that are communicably coupled to the computer (702). Moreover, any or all parts of the API (712) or the service layer (713) may be implemented as child or sub-modules of another software module, enterprise application, or hardware module without departing from the scope of this disclosure.

The computer (702) includes an interface (704). Although illustrated as a single interface (704) in FIG. 7, two or more interfaces (704) may be used according to particular needs, desires, or particular implementations of the computer (702). The interface (704) is used by the computer (702) for communicating with other systems in a distributed environment that are connected to the network (730). Generally, the interface (704) includes logic encoded in software or hardware (or a combination of software and hardware) and operable to communicate with the network (730). More specifically, the interface (704) may include software supporting one or more communication protocols associated with communications such that the network (730) or interface's hardware is operable to communicate physical signals within and outside of the illustrated computer (702).

The computer (702) includes at least one computer processor (705). Although illustrated as a single computer processor (705) in FIG. 7, two or more processors may be used according to particular needs, desires, or particular implementations of the computer (702). Generally, the computer processor (705) executes instructions and manipulates data to perform the operations of the computer (702) and any algorithms, methods, functions, processes, flows, and procedures as described in the instant disclosure.

The computer (702) also includes a memory (706) that holds data for the computer (702) or other components (or a combination of both) that can be connected to the network (730). For example, memory (706) can be a database storing data consistent with this disclosure. Although illustrated as a single memory (706) in FIG. 7, two or more memories may be used according to particular needs, desires, or particular implementations of the computer (702) and the described functionality. While memory (706) is illustrated as an integral component of the computer (702), in alternative implementations, memory (706) can be external to the computer (702).

The application (707) is an algorithmic software engine providing functionality according to particular needs, desires, or particular implementations of the computer (702), particularly with respect to functionality described in this disclosure. For example, application (707) can serve as one or more components, modules, applications, etc. Further, although illustrated as a single application (707), the application (707) may be implemented as multiple applications (707) on the computer (702). In addition, although illustrated as integral to the computer (702), in alternative implementations, the application (707) can be external to the computer (702).

There may be any number of computers (702) associated with, or external to, a computer system containing computer (702), each computer (702) communicating over network (730). Further, the term “client,” “user,” and other appropriate terminology may be used interchangeably as appropriate without departing from the scope of this disclosure. Moreover, this disclosure contemplates that many users may use one computer (702), or that one user may use multiple computers (702).

In some embodiments, the computer (702) is implemented as part of a cloud computing system. For example, a cloud computing system may include one or more remote servers along with various other cloud components, such as cloud storage units and edge servers. In particular, a cloud computing system may perform one or more computing operations without direct active management by a user device or local computer system. As such, a cloud computing system may have different functions distributed over multiple locations from a central server, which may be performed using one or more Internet connections. More specifically, cloud computing system may operate according to one or more service models, such as infrastructure as a service (IaaS), platform as a service (PaaS), software as a service (SaaS), mobile “backend” as a service (MBaaS), serverless computing, artificial intelligence (AI) as a service (AIaaS), and/or function as a service (FaaS).

Although only a few example embodiments have been described in detail above, those skilled in the art will readily appreciate that many modifications are possible in the example embodiments without materially departing from this invention. Accordingly, all such modifications are intended to be included within the scope of this disclosure as defined in the following claims.

Claims

1. A method for selecting lost circulation materials (LCM) for boring a well, the method comprising: determining, for a plurality of LCM blends, a plurality of overall scores based on characteristics associated with each of the plurality of LCM blends; andestablishing a list of LCM blend candidates comprising: including a first of the plurality of LCM blends in a list of LCM blend candidates based on a first overall score associated with the first LCM blend; andexcluding a second of the plurality of LCM blends from the list of LCM blend candidates, based on a second overall score associated with the second LCM blend,wherein the first overall score is greater than the second overall score.

2. The method of claim 1, wherein the characteristics associated with each of the plurality of LCM blends comprise at least one selected from a group consisting of: a stress cage improvement associated with the LCM blend,a fracture tip stress intensity factor improvement associated with the LCM blend, anda financial cost.

3. The method of claim 1, where establishing the list of LCM blend candidates comprises performing an optimization resulting in the first overall score.

4. The method of claim 3, wherein the optimization comprises a Pareto front search.

5. The method of claim 1, further comprising selecting one LCM blend from the plurality of LCM blends on the list.

6. The method of claim 5, wherein the selection is performed based on a weighting of a stress cage improvement associated with the LCM blend vs.a fracture tip stress intensity factor improvement associated with the LCM blend vs. a financial cost.

7. The method of claim 5, further comprising applying the selected LCM blend for the boring of the well.

8. A system for selecting lost circulation materials (LCM) for boring a well, the system comprising: a computer system executing an LCM blend optimization engine that: determines, for a plurality of LCM blends, a plurality of overall scores based on characteristics associated with each of the plurality of LCM blends; andestablishes a list of LCM blend candidates comprising: including a first of the plurality of LCM blends in a list of LCM blend candidates based on a first overall score associated with the first LCM blend; andexcluding a second of the plurality of LCM blends from the list of LCM blend candidates, based on a second overall score associated with the second LCM blend,wherein the first overall score is greater than the second overall score.

9. The system of claim 8, wherein the LCM blend optimization engine further selects one LCM blend from the plurality of LCM blends on the list.

10. The system of claim 9, wherein the selection is performed based on a weighting of a stress cage improvement associated with the LCM blend vsa fracture tip stress intensity factor improvement associated with the LCM blend vs a financial cost.

11. The system of claim 9, further comprising: a drilling system comprising a wellbore, wherein the one selected LCM blend is injected into a drilling mud circulation used for the wellbore.

12. The system of claim 8, wherein the characteristics associated with each of the plurality of LCM blends comprise at least one selected from a group consisting of: a stress cage improvement associated with the LCM blend,a fracture tip stress intensity factor improvement associated with the LCM blend, anda financial cost.

13. The system of claim 8, where establishing the list of LCM blend candidates comprises performing an optimization resulting in the first overall score.

14. The system of claim 11, wherein the optimization comprises a Pareto front search.

15. A non-transitory machine-readable medium comprising a plurality of machine-readable instructions executed by one or more processors, the plurality of machine-readable instructions causing the one or more processors to perform operations comprising: determining, for a plurality of LCM blends, a plurality of overall scores based on characteristics associated with each of the plurality of LCM blends; andestablishing a list of LCM blend candidates comprising: including a first of the plurality of LCM blends in a list of LCM blend candidates based on a first overall score associated with the first LCM blend; andexcluding a second of the plurality of LCM blends from the list of LCM blend candidates, based on a second overall score associated with the second LCM blend,wherein the first overall score is greater than the second overall score.

16. The non-transitory machine-readable medium of claim 15, wherein the characteristics associated with each of the plurality of LCM blends comprise at least one selected from a group consisting of: a stress cage improvement associated with the LCM blend,a fracture tip stress intensity factor improvement associated with the LCM blend, anda financial cost.

17. The non-transitory machine-readable medium of claim 15, where establishing the list of LCM blend candidates comprises performing an optimization resulting in the first overall score.

18. The non-transitory machine-readable medium of claim 17, wherein the optimization comprises a Pareto front search.

19. The non-transitory machine-readable medium of claim 15, further comprising selecting one LCM blend from the plurality of LCM blends on the list.

20. The non-transitory machine-readable medium of claim 19, wherein the selection is performed based on a weighting of a stress cage improvement associated with the LCM blend vsa fracture tip stress intensity factor improvement associated with the LCM blend vs a financial cost.