Optimization runs - Optimization results - Joint Optimization of Well Placement and Controls Ap

Joint Optimization of Well Placement and Controls Applied to a Real Field Case

3.5 Optimization results

3.5.1 Optimization runs

Our optimization procedure has developed a total of nine well placement and control so-lutions using the AD–GPRS work model. Table 3.5 shows the final objective function values for these solutions, in addition to the corresponding well lengths. Function evolu-tion graphs for sequential and joint soluevolu-tions are presented in figures 3.16 and 3.17 (these figures will be described in further detail at a later point). The graphs presented in these figures correspond to joint and sequential optimization runs using different configurations of projection operators. That is, these runs are the result of launching different config-urations of the optimization procedure described in Algorithm 3 (page 90). Of the nine solutions, four are obtained using the sequential approach, while five solutions were devel-oped using the joint approach. Joint and sequential runs are denoted by names "JNT" and

"FXD", respectively. Additionally, the name for each individual solution indicates which conﬁguration of projection operators (see Section 3.4.3: Non–linear constraint handling, page 84) that was applied during the optimization. While the use of well–bound and inter–

well distance operatorsPrbandPwdis the same for all solutions, there are four possible implementations for the well–length operatorPwl: "OPT2", "OPT", and "CUT" corre-spond toP_wl^A,P_wl^B, andP_wl^Cbeing applied during the optimization procedure, respectively, while "M1" means that aP_wl^Cprojection (with an added lower bound) is applied only once, at the end of the optimization iteration.

Description of results shown in Table 3.5. Table 3.5 shows the final objective func-tion values (FOPT), total number of reservoir simulafunc-tions (nsims), and corresponding well lengths for each of the nine solutions. Well lengths for all solutions are shown in columns 4 to 7. Solution values associated with the sequential and joint approaches are shown in the upper and lower half of the table, respectively. For comparison, values as-sociated with the initial well configuration, referred to as “BASECASE”, are given in the first row in Table 3.5. All objective function values in this table are normalized

rel-Table 3.5: FOPT and well lengths corresponding to the well placement solutions for joint and sequential runs. The total number of reservoir simulations run by at each optimization is also given. The upper level title “AD–GPRS₁₂₀₀” refers to the fact that the results in this table are obtained using the AD–GPRS work model approximation, run using the1200 day production time frame. Initial base case values for FOPT and well lengths are provided for comparison.

Solution AD–GPRS₁₂₀₀

FOPT Well lengths [m]

x^∗_p, x⁰_c x^∗_p, x^∗_c

nsims WL.-A WL.-B WL.-C WL.-D

BASECASE 1.000 1.011 - 1439 1247 1409 0874

FXD1M1 1.193 1.208 0708 1500 1500 1500 1200

FXD2OPT 1.298 1.315 1144 1364 1139 1299 1358

FXD2OPT2 1.268 1.272 1343 1129 1202 1295 1362

FXD2CUT 1.225 1.234 0905 1129 1202 1373 1069

JNT2M1 - 1.334 5692 1500 1500 1500 1200

JNT1M1 - 1.308 2873 1500 1500 1500 1200

JNT2OPT - 1.329 9727 1088 1474 1299 1316

JNT2OPT2 - 1.307 9633 1129 1142 1302 1155

JNT2CUT - 1.294 6798 1129 1034 1299 1271

ative to the initial FOPT value for BASECASE, i.e., FOPT(x⁰_p, x⁰_c). For the sequential approach, we also provide the objective function values before the ﬁnal optimization of well controls, i.e., FOPT(x^∗_p, x⁰_c), and after control optimization, i.e., FOPT(x^∗_p, x^∗_c). Note that runs JNT2M1, JNT1M1 and FXD1M1 were developed without restrictions on well length during optimization. However, once these iterations ﬁnished, the well lengths cor-responding to these solutions were projected onto upper and lower bounds of 1500 and 1200 meters. The objective function values for these solutions were then recalculated us-ing the new well lengths. In the followus-ing, we will compare the joint and sequential runs.

(The JNT1M1 run is not included in this comparison because this solution is an alternative version of the JNT2M1 run, which will be discussed separately at a later point.)

Joint vs. sequential approach: Comparison of mean FOPT values from Table 3.5.

For the sequential runs, the second column values, i.e., FOPT(x^∗_p, x^∗_c), in the upper half of Table 3.5, result from an optimization of controls performed after a well placement con-figuration has been found using fixed controls. Taking the mean of these values, and com-paring them to the initial well placement and control configuration (first–column FOPT value corresponding to BASECASE), we obtain a mean increase in FOPT of almost 26%

for the sequential runs. Continuing towards the bottom half of Table 3.5, we see that the corresponding joint solutions have higher FOPT increases than their sequential counter-parts. The FOPT(x^∗_p, x^∗_c) values for the joint solutions result in a mean increase in FOPT of close to 33% over the initial conﬁguration. In summary, we have that for this particu-lar problem case, the solutions obtained using the joint approach yield, on average, a 7%

higher FOPT increase compared to the solutions obtained using the sequential approach.

Inﬂuence of control optimization in each approach. For each of the sequential opti-mization runs, the increase due to the ﬁnal optiopti-mization of controls is found by compar-ing the values in the FOPT(x^∗_p, x⁰_c) column against those in the FOPT(x^∗_p, x^∗_c) column.

From Table 3.5 we have that the mean increase these runs receive due to their sole opti-mization of controls at the end is somewhat above 1% (Roughly, the increases are 1.5%

for FXD1M1, 1.7% for FXD2OPT, 0.4% for FXD2OPT2 and 0.9% for FXD2CUT.) These increases are comparable to the increase obtained when optimizing the controls us-ing the initial well configuration (which is 1.1%, see first row). For this particular problem case then, we have that the yields from control optimization when using the initial well configuration, or any of the final well configurations obtained from the sequential runs, are modest. Interestingly, from function evolution data for the joint runs, we observe that the control routine when embedded within the well placement search, yields, on average, considerable higher increases when applied over whole ranges of different well place-ment trial solutions. For all the trial solutions in each of the joint runs, Table 3.6 shows the mean increases in FOPT associated with the control optimization part only. Corre-sponding standard deviations are also given. Roughly, the data show that when using the joint approach, each well placement trial solution obtains a general improvement in ob-jective function value due to the inner–loop control procedure of, on average, 7.5%. This could be an indication that the control routine is compensating for less promising well locations, as also noted for the much simpler case in Section 2.4.1 (page 26). A final note is that the mean FOPT increase due to the embedded optimization of controls is main-tained throughout the entire well placement search for all joint runs. However, we see from the relatively large standard deviations for all runs in Table 3.6 that the magnitude

of the contributions is unevenly distributed among the trial solutions.

Table 3.6: Mean increases in FOPT resulting from embedded control optimization during joint runs.ΔFOPT^tsolns_cntrl represents the mean FOPT increase associated with the control optimization part only, averaged over all trial solutions (tsolns) that are performed during a joint run.

Solution ΔFOPT^tsolns_cntrl

[%] σ

JNT2M1 7.6 3.8

JNT2OPT 7.6 3.2

JNT2OPT2 8.5 4.0

JNT2CUT 6.2 3.7

Mean 7.5 3.7

Cost of joint vs. sequential approach. The gains obtained from the implementation of the joint approach must be balanced against the computational cost involved in performing the additional control optimizations. Due to the embedded control routines, the joint ap-proach is significantly more costly in terms of total number of reservoir simulations that need to be performed during the optimization procedure. From Table 3.5 we have that the mean total number of reservoir simulations required by the joint runs is almost 7 times higher than the mean number of reservoir simulations required by the sequential runs.

This is an important factor for real ﬁeld applications where reservoir simulations are very time consuming. In such a case, a sequential approach might be a better choice to optimize for well locations.

Inﬂuence of different well–length constraint implementations. Different implementa-tions of the well–length constraint have been used in this work (see Section 3.4.3: Non–

linear constraint handling, page 84). A pair of joint and sequential optimization runs have each been launched using one of four possible implementations for the well–length op-eratorPwl. Using Table 3.5, we conﬁrm that, as expected, the joint solutions are higher than their sequential counterparts within each of these joint–sequential pairs of runs. This result is interesting because, in practical applications, users may launch the optimization procedure using very different well–length constraint formulations.

Trade–off between constraints and approaches. For expensive problems, an attractive option to accelerate the optimization process might be to launch an optimization run using the conﬁguration that only imposes the well–length constraint at the end of the iteration, i.e., the “M1” implementation. From Table 3.5 we see that the M1 solutions require the least number of reservoir simulations for each type of approach, i.e., 5692 and 708 for the joint and sequential M1 solutions, respectively. However, while the joint M1 solution yields the highest FOPT among all solutions in our case, the M1 solution corresponding to the sequential approach yields the lowest ﬁnal objective function value. Not enough data is available to support a choice, but based only on the two current data points, it appears the simpler implementation of the well–length constraint is more amenable for

use when performing an optimization using the joint approach. Alternatively, the more sophisticated well–length constraint implementations (e.g., “OPT2” or “OPT”) could be chosen using either approach. From a practical point of view, even though using either one of these constraint implementations increases the cost of the optimization, there seems to be no major difﬁculty in applying them with the less costly sequential approach to achieve acceptable results.

Function evolution graphs. The trade–off between greater FOPT and computational cost is apparent when comparing the function evolution graphs for both approaches. Fig-ures 3.16 and 3.17 both show the objective function evolution graphs for all the joint and sequential runs. In each figure, the joint and sequential function evolution curves are plot-ted in separate graphs (curves corresponding to the sequential solutions are plotplot-ted on the left graphs while curves from the joint runs are plotted on the graphs to the right). In Figure 3.16, all function evolution curves are plotted with respect to the number of objec-tive function evaluation calls. In Figure 3.17, the same function evolution data is plotted but this time with respect to the cumulative number of reservoir simulations performed during the optimization procedure (recall that in the joint approach an objective function evaluation is equal to a control optimization that typically requires several reservoir sim-ulations). Consequently, Figure 3.16(a) is equal to Figure 3.17(a), since a cost function evaluation for the sequential approach only requires one reservoir simulation. (Though equal, we plot both graphs to complement the joint–sequential figure array.) Taking par-ticular note of the x–axis scaling in these figures, we confirm the substantially higher cost of the optimization procedure, in terms of total number of reservoir simulations, when the procedure uses the joint approach compared to when it uses the sequential approach.

0 200 400 600 800 1000 1200 1400

Figure 3.16: Function evolution as a function of number of function calls for 3.16(a) sequential and 3.16(b) joint runs.

Performance cost function evolution graphs. From the graphs in Figure 3.16 we can compare the function evolutions curves corresponding to the sequential solutions, shown in Figure 3.16(a), with the function evolution curves for the joint solutions, shown in Figure 3.16(b). These curves are plotted with respect to the number of objective func-tion evaluafunc-tions performed by the well placement optimizafunc-tion part of the procedure. The comparison is then made in terms of cost function calls for each of the two approaches.

0 200 400 600 800 1000 1200 1400

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000

1.150

Figure 3.17: Function evolution as a function of total number of reservoir simulations for 3.17(a) sequential and 3.17(b) joint runs.

Comparing these graphs we notice each joint solution displays a better–performing func-tion evolufunc-tion curve, in terms of quicker progression and higher ﬁnal FOPT, than their corresponding sequential cost function curve.

Decrease in FOPT drop due to M1 well–length constraint implementation. From figures 3.16(a) and 3.16(b) we can also observe the drop in final FOPT caused by imposing the well–length restriction after the optimization iteration has ended (as mentioned, this is the “M1” enforcement of the maximum well–length). The M1 configuration is applied in optimization run FXD2M1 in Figure 3.16(a), and in run JNT2M1 in Figure 3.16(b).

(Since run JNT1M1 is a different version of JNT2M1, the general discussion regarding the latter also applies to the former. However, the JNT1M1 run is discussed separately below). Notice that a new optimization of controls is performed using the projected well conﬁguration with shorter well lengths. From the application of “M1” we notice the close link between well length and oil recovery, in that the reduction in well lengths causes a significant drop in FOPT for both solutions. Interestingly, the drop in FOPT is less for the joint JNT2M1 run than for the sequential FXD2M1 run. (Several more data points would be needed to further extrapolate based on these results⁷.)

Comparison of runs JNT2M1 and JNT1M1. Among the joint runs, JNT1M1 is a spe-cial run in that it was launched using a setting of only 3 major iterations in the SNOPT solver used to perform the embedded control optimization. All other joint runs, on the other hand, were run using 6 major iterations in the embedded control routine. By com-paring the JNT1M1 and JNT2M1 curves in Figure 3.16(b), we conﬁrm that the two runs require close to the same number of objective function evaluations. Moreover, we have from Table 3.5 that the JNT2M1 solution, as expected, requires almost twice as many reservoir simulations, 5692 in total, as the JNT1M1 run, which performs 2873 reservoir simulations. This relationship is clearly seen in Figure 3.17(b).

The JNT1M1 run was launched to test whether a setting of only 3 major iterations in SNOPT was enough to optimize for controls at various well conﬁgurations. (Control optimizations embedded within the well placement search usually stop due to the tight major iteration limit imposed on the SQP implementation.) In this regard, we see the

Table 3.7: Performance, in terms of normalized FOPT, of solutions obtained using the AD–

GPRS work model (column one) when transferred to the Eclipse field case model (columns two and three). Columns two and three result from the first and second type of transfer, respec-tively. The first type of transfer refers to the implementation of both optimized well placement and controls, while the second refers to the implementation of only the solution well configurations alongside original field case simulator settings (x^S_c). Results in the second and third column are normalized with respect to the FOPT obtained when running the BASECASE well configuration using field case simulator settings for controls (this value is found in the first row in column three of that table). The upper level title “FOPT₁₂₀₀” refers to the fact that the results in this table show the FOPT obtained using the AD–GPRS work model approximation and the Eclipse field case model, both run for the1200 day production time frame.

Solution FOPT₁₂₀₀

AD–GPRS ECLIPSE

x^∗_p, x^∗_c

x^∗_p, x^∗_c x^∗_p, x^S_c

BASECASE 1.011 0.984 1.000

FXD1M1 1.208 1.125 1.133

FXD2OPT 1.315 1.229 1.235

FXD2OPT2 1.272 1.195 1.219

FXD2CUT 1.234 1.156 1.176

JNT2M1 1.334 1.249 1.255

JNT2OPT 1.329 1.243 1.249

JNT2OPT2 1.307 1.230 1.233

JNT2CUT 1.294 1.215 1.215

lower setting performs sufﬁciently well to yield a function evolution curve comparable to the one from JNT2M1. However, the decrease in FOPT after the well–length constraint is imposed, is significantly larger for the JNT1M1 run than for the JNT2M1 solution. More data would be needed from this test case to make further associations regarding how this setting may affect joint runs with different constraint implementations. Notice, however, that this discussion is related to our previous observation in Section 2.4.2 (page 34), re-garding how an insufﬁcient maximum number of major iterations may yield suboptimal solutions. Finally, a setting of 6 major iterations was chosen for the application of the joint approach because this setting was considered more stable with respect to the different im-plementations of the well–length constraint and the range of different well placement trial solutions.

Transfer of results to Eclipse ﬁeld case model

As stated in the beginning of this chapter, Section 3.1: Targets and strategy for application development (page 42), one of the main targets for this optimization effort has been to test the application of our methodology on a real field case. At this point, we therefore shift our attention to focus on transferring the solutions obtained using the work model on to the original Eclipse field case model. As treated previously in Section 3.3.3: Field model transfer and validation (page 69), various approximations to original simulator functions and model properties have been introduced into the work model. Consequently, these approximations are an inherent part of the different solutions obtained using the optimization framework. Due to the differences in the work model, a general decrease in gains might be expected once the solutions are tested on the more complex, original field case model running on the commercial simulator.

On a separate note, we have up to now described the results obtained from the opti-mization procedure only in terms of performance, but have not yet treated the work model solutions in reservoir engineering terms. Rather, in this work we perform this type of anal-ysis (e.g., presentation of solution well trajectories with relevant production profiles) only after the different solutions have been implemented on the original field case model. On this point, we have decided based on our second strategy component that emphasizes making as much of the current application effort as possible, accessible to the field devel-opment work process of the operator. For this purpose, we devote the entire next chapter, Chapter 4, to testing and analyzing all solutions subject to various considerations impor-tant within the perspective of field development operations. In the following, we present test results from the transfer of solutions on to the original Eclipse field case model. As a prelude to the next chapter, at the end of this section we also present saturation maps corresponding to the best–performing solution from the transfer process.

Description of transfer table. In Table 3.7 we compare how solutions obtained using the AD–GPRS work model perform when transfered to the Eclipse field case model, again validated for the 1200 day production scenario. As briefly commented, it is im-portant to note that because the AD–GPRS work model is an approximation to the field case model, some of the gains achieved by the optimization procedure using the work model, are likely to decrease once the solutions are transferred to the Eclipse model. For reference, in column one of Table 3.7, we again show the cost function values obtained using the optimization procedure (these values have previously been presented in column two of Table 3.5). The solutions obtained from the optimization procedure are imple-mented in the field case model in two ways, or in two different types of transfer. In the first type of transfer, both the well placement part of the solution, i.e.,x^∗_p, and the op-timized well control settings, i.e.,x^∗_c, are implemented in the Eclipse field case model.

In a second type of transfer, only the well placement part of the solution is implemented in the ﬁeld case model. For both types of transfers, to implement the well placement

In document Joint Optimization of Well Placement and Controls for Petroleum Field Development (Page 111-123)