Journal of Petroleum & Environmental Biotechnology

Journal of Petroleum & Environmental Biotechnology
Open Access

ISSN: 2157-7463

+44 7480022449

Review Article - (2019) Volume 10, Issue 5

A Novel Workflow for Using Fiber-Optic Telemetry-Enabled Coiled Tubing in Candidate Selection

Ahmed El-Attar*
 
*Correspondence: Ahmed El-Attar, Cairo University, Governorate 12613, Egypt, Tel: +20 2 35676105, Email:

Author info »

Abstract

Formation damage is an undesirable operational and economic problem that can occur throughout the lifecycle of oil and gas wells due to several reasons such as using incompatible fluids during workover operations, fines migration, clay swelling, emulsions formation, and scale and organic depositions. Also, newly drilled wells sometimes do not produce optimally due to the damages caused by the drilling fluids. Therefore, addressing formation damage issues to ensure optimum recovery of hydrocarbons needs more efforts on identifying the damage mechanism and quantifying the skin factor. Skin factor is a dimensionless number that reflects the production impairment due to near-wellbore reduction of permeability. So, if this number is zero it means the well is intact, however; if this number is positive that means the well is damaged. The workflow presented in this paper focuses on the use of fiber-optic telemetry-enabled coiled tubing (FOTECT) for production enhancement in real-time by quantifying skin factor, estimating the flow potential and determining the candidate wells for matrix stimulation. This new technology can deliver pressure data in real-time during a typical unloading operation that could be further used in well test analysis for estimating key reservoir properties such as skin (S), flow capacity (Kh), drainage area (A) and initial reservoir pressure (Pi). The new technology reduces the operational time required for well test analysis compared with conventional downhole recording systems (DHR) by two-fold while enabling the performance of an acid treatment in the same run.

Moreover, in this study a workflow and user-interface software using java language were developed to execute the workflow through a two-step streamlined process:

1. Assessing the well damage through quantifying the skin value from pressure transient analysis (PTA) utilizing the downhole pressure data acquired from coiled tubing in real-time.

2. Inflow performance relationship (IPR) construction of the well using Vogel’s correlation and productivity index equation under the current condition and under ideal condition (Zero skin) to assess the feasibility of a stimulation treatment.

The paper will present the application of this technique on simulated field data to show how FOTECT could be used to diagnose and treat the well in the same run. The output obtained from the developed software will be compared against the output of an industry popular well-test suite (Sapphire). Also, a case study in which this technology was used for pressure transient analysis for artificial lift design will be presented to show the applicability of this novel approach and to prove it can yield matching results with conventional techniques in a more efficient way. From the simulated data the developed software estimated the skin factor to be nine from both build-up and draw down analysis, which was later matched by Sapphire commercial Suite; moreover, it was shown that the current production rate of 792-BPOD can be increased to 1722-BOPD post a successful stimulation treatment.

Keywords

Fiber-optic telemetry-enabled coiled tubing; Flow capacity (Kh); Drainage area (A); Initial reservoir pressure (Pi); Conventional downhole recording systems (Dhr)

Abbrevations

CCL: Casing Collar Locator; Ct: Total compressibility, psia-1; CP: Centipoise; CSV: Comma separated value; CT: Coiled Tubing; DHR: Downhole recording; DST: Drill Stem Test; FOTECT: Fiber-Optic Telemetry-Enabled Coiled Tubing; ft: foot; h: Net pay thickness, ft; IARF: Infinite Acting Radial Flow; IPR: Inflow Performance Relationship; J: Productivity index, STB/D-Psi; k: Permeability, md; md: Milli-Darcy; P1 hr: Pressure at 1-hr reading on linear graph, psi; PI: Productivity index, STB/D-Psi; Pi: Initial reservoir pressure, psi; Pr: Average reservoir pressure, psia; PTA: Pressure Transient Analysis; Pwf: Flowing bottom hole pressure, psia; Qo: Oil Production rate, STB/d; Re: Drainage radius; ft; rw: Wellbore radius, ft; S Skin factor, Dimensionless; tp: Production time; hrs; UOM: Unit of Measurement; Δt: Shut-in time, hrs; βo: Oil Formation Volume factor, STB/ res, bbl; μo: Oil viscosity; cp; ∅: Porosity Fraction

Introduction

Nitters et al. [1] indicated that 60 to 70% of matrix stimulation treatments fail worldwide due to a lack of structured approach in candidate selection and treatment design. This high percentage of failure represents millions of dollars wasted due to choosing the wrong candidate and/or a bad design of the stimulation treatment. So, the motive for this study is to introduce and develop a new concept and workflow for performing candidate selection and well treatment simultaneously. This workflow will leverage the capability of the new generation of coiled tubing units of downhole pressure data transmission via fiber-optic telemetry. Acquiring such real-time pressure data will allow the operator to perform real-time pressure transient analysis (PTA), and based on the analysis results, skin factor could be calculated. In case it was concluded that formation damage is present as indicated by appositive skin factor, the same coiled tubing unit could be further used to treat the damage through a well-designed stimulation treatment in the same run saving logistical cost and time. So coiled tubing will be used for both candidate selection and remedial work simultaneously.

Conventionally During pressure transient analysis downhole parameters are commonly measured and registered using downhole memory gauges, which can only be retrieved and analyzed after the end of the well test. The main drawback of this approach is that fluid mobility (K/μ) is usually a key uncertainty before conducting the test. So, test sequence and durations cannot be planned precisely. this jeopardizes the accuracy of the test results, Since, an early-terminated test will yield incomplete set of data resulting in an inaccurate result or the need of repeating the test. And, an unnecessarily-extended test will add up extra costs associated with rig time and unnecessary flaring.

The proposed workflow overcomes the drawbacks of the downhole recording mode (DHR) by conducting dynamic and real-time pressure transient analysis. The real-time pressure data obtained from the pressure sensor deployed with the coiled tubing is the key input required for pressure transient analysis. It can be obtained with coiled tubing during a typical Nitrogen lifting operation (Drawdown analysis), or while the well is shutin and by using an inflatable packer to minimize wellbore storage effects (Pressure build-up analysis). The main advantage over the downhole recording mode (DHR) through memory gauges is that the pressure data is monitored in real-time. So, the well test engineer can adjust the test sequence by identifying wellbore storage period, infinite acting radial flow (IARF) and reaching the boundary. Those benefits will save time and ensure accurate well test analysis [2]. Additionally, the same coiled tubing unit can be used in matrix acidizing treatment to remove skin if test results indicate the presence of formation damage After quantifying the skin factor and ensuring it is due to formation damage (i.e., no mechanical damage is present). Petroleum engineers ensure the effectiveness of the stimulation treatment by studying the inflow performance relationship (IPR) of the well. This step is done to check the production gain value in case skin was removed. The built model performs both tasks which are pressure transient analysis and inflow performance comparison and concludes if the well is a good candidate for matrix stimulation.

Literature Review

Model description and approach

The workflow consists of two basic modules that integrates with each other and are used in series to conclude the final output of the software which is whether a well is candidate for matrix stimulation or not.

The petroleum engineer should confirm that the resulted skin value is only attributable to formation damage (i.e., No pseudoskin) and the economic gain as indicated from the software justifies the expenses associated with carrying out a matrix treatment (costbenefit analysis).

The first module is named “Pressure transient analysis” and is used to carry out the pressure transient analysis by importing the pressure data acquired from the fiber-optic telemetry-enabled coiled tubing (FOTECT) downhole pressure gauges in real-time. They are either input manually as or imported from a comma separate value (CSV) format file. The software can carry-out PTA either in build-up or drawdown mode. It is recommended in build-up analysis to add an inflatable packer to the bottom hole assembly to minimize the wellbore storage effects.

The results obtained from the first module are used to feed the second module which is named “Treatment feasibility study” and is used to perform reservoir performance analysis. It constructs the inflow performance relationship (IPR) of the well in its current condition and under assumed ideal condition (Zero skin). The IPR is either constructed using straight-line productivity index equation or Vogel’s correlation to suit different well conditions and account for the scenario in which the flowing bottom hole pressure (Pwf) is below the bubble point pressure (Pb). After quantifying the skin (S) and obtaining the average permeability thickness (Kh) from module one and calculating the gain that could be achieved if the skin value is brought to zero by a stimulation treatment from module 2. The final decision maker function of the software concludes if the well is a good candidate for matrix stimulation.

Figures 1 and 2 show a flowchart describing the workflow in drawdown and build-up modes respectively.

petroleum-environmental-biotechnology-methodology

Figure 1: Candidate selection using FOTECT methodology: Drawdown mode.

petroleum-environmental-biotechnology-selection

Figure 2: Candidate selection using FOTECT methodology: Pressure build-up mode.

Input data

In addition to the real-time pressure data acquired in real-time through the FOTECT, the following data will be needed to run the software [3,4]:

a) Porosity – Fraction

b) Wellbore Radius (rw) – ft

c) Reservoir height (h) – ft

d) Oil formation volume factor βo– res. bbl/STB

e) Oil viscosity μ– cp

f) Initial pressure (Pi) – psia

g) Flow rate (Q) – STB/D

Pressure transient analysis

The model is based on the analytical solution of partial differential equation that describes the fluid flow in the reservoir as a function of time and space (diffusivity equation).

The software uses the semi-log analysis technique to calculate skin and permeability using the below equations:

For Draw Down mode:

image

For Build-up mode:

image

Treatment gain estimation

After skin value is obtained, it should be confirmed that no mechanical damage is present.

Table 1 shows McLeod criteria to identify pseudo skin and identify the treatable skin which is the target of this stud, and then the software uses both straight line productivity index equation and Vogel’s correlation to construct the inflow performance relationship of the well under current conditions and ideal condition (zero skin). This step quantifies the production gain that could be realized if the well damage is treated through a stimulation operation.

Parameters Value
High liquid gas ration LGR >100 bbl/MMCF (Gas well)
High Gas oil ratio GOR  >1000 Scf/bbl (Oil well)
Three Phase Production (Water, oil and gas)
High pressure Drawdown (Pr-Pwf) > 1000 psi
High flow rate Q/h > 20 BPD/ft
Production rate per perforation shot density Q/N > 5 BPD/Perf.
Perforation shot density < 4 SPF
Perforation Phasing Zero degree phasing
Perforation with Small through tubing Gun Gun diameter less than 2 inches
Reservoir pressure >Pb; While well-bore pressure <Pb

Table 1: McLeod guidelines to distinguish damages associated to mechanical issues.

For cases where the flowing bottom whole pressure (Pwf) is below the reservoir bubble point pressure (Pb) the straight-line productivity index equation is used to construct the IPR curves.

The software calculates pre-treatment productivity index and post treatment productivity index (PI) from the output data obtained from the first step using the below equations:

image (5)

image (6)

Where St in Eq. 6 is set to Zero by default (ideal condition).

Productivity ratio is then estimated which indicates the degree of damage in the well and a very good indicator for candidate selection.

image (7)

Then the oil production rate can be calculated using the basic productivity index equation eq. 8 in the current condition and ideal condition, consequently production gain could be estimated.

image (8)

The two IPR curves can be plotted on the same sheet to compare and quantify the gain that could be achieved post a successful stimulation treatment that gets skin to zero.

To account for cases in which flowing bottom hole pressure is below the reservoir fluid bubble point pressure. Vogel’s correlation is used to predict the IPR using equations 9 and 10:

image (9)

Where:

image (10)

The composite IPR graph is then plotted for the pre-treatment curve and another one for the post-treatment IPR curve for the petroleum Engineer to compare and assess the production gain post a stimulation treatment.

Final output: Candidate selection advisor

The final output is a decision-making window indicating whether this well is a candidate for matrix stimulation or not based on skin value, this workflow assumes that the total skin calculated using pressure transient analysis techniques is only attributed to formation damage with zero mechanical (Pseudo-skin).

In case there’s positive skin (Formation damage present) the message generated to the user from the decision-maker module of the software will be that the well is a candidate for matrix stimulation if there’s no pseudo-skin and economic feasibility is assured as shown in the below Figure 3; however, if the skin value is negative the software will hint to the user that an improved wellbore condition is observed as shown in Figure 4.

petroleum-environmental-biotechnology-negative

Figure 3: Candidate selection advisor output: Positive/negative skin.

petroleum-environmental-biotechnology-properties

Figure 4: Inputs of well (A) properties (Software window).

Model validation

A set of simulated field data (Pressure Vs. time) for pressure build up and drawdown was used to validate the software results against commercial industry software, the output delivered from the commercial software will be compared against the output of the developed model.

This validation case proves the capability of this workflow to conclude if a well is a good candidate for matrix stimulation as an output using FOTECT that can be further used to remove the damage in the same run through a well-designed stimulation treatment saving time and cost while delivering better results than the conventional techniques for well testing (DHR).

Validation case: Well (A)

Well (A) is producing from an oil reservoir. A set of well-test simulated data for both pressure build-up and draw-down analysis is used for executing the workflow. The set of data is assumed to be obtained from the FOTECT (Pressure Vs Time) and is presented in Table 2 along with the input parameters (reservoir properties, oil properties and production parameters) in Tables 3 and 4 required for pressure transient analysis and inflow performance relationship construction [5].

Time Pressure Liquid Rate Time Pressure Liquid Rate Time Pressure Liquid Rate
(hr) (psia) (STB/D) (hr) (psia) (STB/D) (hr) (psia) (STB/D)
- 4,997 1000 108.03 569 1000 213.53 4,655 0
0 4,984 1000 109.53 568 1000 215.03 4,668 0
0.01 4,972 1000 111.03 566 1000 216.53 4,680 0
0.01 4,960 1000 112.53 564 1000 218.03 4,690 0
0.01 4,947 1000 114.03 563 1000 219.53 4,700 0
0.02 4,935 1000 115.53 561 1000 221.03 4,709 0
0.02 4,923 1000 117.03 560 1000 222.53 4,717 0
0.02 4,911 1000 118.53 558 1000 224.03 4,724 0
0.02 4,898 1000 120.03 557 1000 225.53 4,731 0
0.03 4,886 1000 121.53 555 1000 227.03 4,738 0
0.03 4,873 1000 123.03 554 1000 228.53 4,744 0
0.03 4,858 1000 124.53 552 1000 230.03 4,750 0
0.04 4,842 1000 126.03 551 1000 231.53 4,755 0
0.04 4,823 1000 127.53 549 1000 233.03 4,760 0
0.05 4,803 1000 129.03 548 1000 234.53 4,765 0
0.05 4,780 1000 130.53 547 1000 236.03 4,770 0
0.06 4,754 1000 132.03 545 1000 237.53 4,774 0
0.07 4,726 1000 133.53 544 1000 239.03 4,778 0
0.08 4,694 1000 135.03 543 1000 240.53 4,782 0
0.09 4,659 1000 136.53 541 1000 242.03 4,786 0
0.1 4,620 1000 138.03 540 1000 243.53 4,789 0
0.11 4,577 1000 139.53 539 1000 245.03 4,793 0
0.12 4,529 1000 141.03 537 1000 246.53 4,796 0
0.14 4,477 1000 142.53 536 1000 248.03 4,799 0
0.15 4,418 1000 144.03 535 1000 249.53 4,802 0
0.17 4,354 1000 145.53 534 1000 251.03 4,805 0
0.19 4,284 1000 147.03 532 1000 252.53 4,808 0
0.24 4,124 1000 150.03 530 1000 255.53 4,814 0
0.27 4,032 1000 151.53 529 1000 257.03 4,816 0
0.3 3,933 1000 153.03 528 1000 258.53 4,819 0
0.34 3,826 1000 154.53 526 1000 260.03 4,821 0
0.38 3,711 1000 156.03 525 1000 261.53 4,823 0
0.43 3,588 1000 157.53 524 1000 263.03 4,826 0
0.48 3,458 1000 159.03 523 1000 264.53 4,828 0
0.54 3,319 1000 160.53 522 1000 266.03 4,830 0
0.6 3,174 1000 162.03 521 1000 267.53 4,832 0
0.68 3,023 1000 163.53 520 1000 269.03 4,834 0
0.76 2,866 1000 165.03 519 1000 270.53 4,836 0
0.85 2,706 1000 166.53 518 1000 272.03 4,838 0
0.96 2,545 1000 168.03 516 1000 273.53 4,840 0
1.07 2,383 1000 169.53 515 1000 275.03 4,842 0
1.21 2,223 1000 171.03 514 1000 276.53 4,843 0
1.35 2,068 1000 172.53 513 1000 278.03 4,845 0
1.52 1,918 1000 174.03 512 1000 279.53 4,847 0
1.7 1,778 1000 175.53 511 1000 281.03 4,848 0
1.91 1,647 1000 177.03 510 1000 282.53 4,850 0
2.14 1,528 1000 178.53 509 1000 284.03 4,851 0
2.41 1,421 1000 180.03 508 1000 285.53 4,853 0
2.7 1,326 1000 181.53 507 1000 287.03 4,854 0
3.03 1,244 1000 183.03 506 1000 288.53 4,856 0
3.4 1,175 1000 184.53 505 1000 290.03 4,857 0
3.81 1,116 1000 186.03 504 1000 291.53 4,859 0
4.28 1,066 1000 187.53 503 1000 293.03 4,860 0
4.8 1,025 1000 189.03 502 1000 294.53 4,861 0
5.39 991 1000 190.53 501 1000 296.03 4,863 0
6.04 962 1000 192.03 501 1000 297.53 4,864 0
6.78 938 1000 193.53 500 1000 299.03 4,865 0
7.61 916 1000 195.03 499 1000 300.53 4,866 0
8.54 897 1000 196.53 498 1000 302.03 4,868 0
9.58 879 1000 198.03 497 1000 303.53 4,869 0
10.75 862 1000 199.53 496 1000 305.03 4,870 0
12.06 846 1000 199.77 496 1000 306.53 4,871 0
13.53 830 1000 200 496 1000 308.03 4,872 0
15.03 816 1000 200 508 0 309.53 4,873 0
18.03 792 1000 200.01 533 0 312.53 4,875 0
19.53 782 1000 200.01 545 0 314.03 4,876 0
21.03 772 1000 200.01 558 0 315.53 4,877 0
22.53 763 1000 200.02 570 0 317.03 4,878 0
24.03 755 1000 200.02 582 0 318.53 4,879 0
25.53 747 1000 200.02 594 0 320.03 4,880 0
27.03 740 1000 200.03 606 0 321.53 4,881 0
28.53 733 1000 200.03 620 0 323.03 4,882 0
30.03 726 1000 200.03 634 0 324.53 4,883 0
31.53 720 1000 200.04 651 0 326.03 4,884 0
33.03 714 1000 200.04 669 0 327.53 4,885 0
34.53 709 1000 200.05 690 0 329.03 4,886 0
36.03 703 1000 200.05 713 0 330.53 4,886 0
37.53 698 1000 200.06 738 0 332.03 4,887 0
39.03 693 1000 200.07 767 0 333.53 4,888 0
40.53 689 1000 200.08 798 0 335.03 4,889 0
42.03 684 1000 200.09 833 0 336.53 4,890 0
43.53 680 1000 200.1 872 0 338.03 4,890 0
45.03 676 1000 200.11 916 0 339.53 4,891 0
46.53 672 1000 200.12 963 0 341.03 4,892 0
48.03 668 1000 200.14 1,016 0 342.53 4,893 0
49.53 664 1000 200.15 1,074 0 344.03 4,893 0
51.03 660 1000 200.17 1,138 0 345.53 4,894 0
52.53 657 1000 200.19 1,208 0 347.03 4,895 0
54.03 653 1000 200.21 1,285 0 348.53 4,896 0
55.53 650 1000 200.24 1,369 0 350.03 4,896 0
57.03 647 1000 200.27 1,460 0 351.53 4,897 0
58.53 643 1000 200.3 1,559 0 353.03 4,898 0
60.03 640 1000 200.34 1,666 0 354.53 4,898 0
61.53 637 1000 200.38 1,781 0 356.03 4,899 0
63.03 634 1000 200.43 1,904 0 357.53 4,900 0
64.53 632 1000 200.48 2,035 0 359.03 4,900 0
66.03 629 1000 200.54 2,173 0 360.53 4,901 0
67.53 626 1000 200.6 2,318 0 362.03 4,901 0
69.03 623 1000 200.68 2,470 0 363.53 4,902 0
70.53 621 1000 200.76 2,626 0 365.03 4,903 0
72.03 618 1000 200.85 2,786 0 366.53 4,903 0
75.03 613 1000 201.07 3,109 0 369.53 4,904 0
76.53 611 1000 201.21 3,269 0 371.03 4,905 0
78.03 608 1000 201.35 3,424 0 372.53 4,906 0
79.53 606 1000 201.52 3,573 0 374.03 4,906 0
81.03 604 1000 201.7 3,714 0 375.53 4,907 0
82.53 602 1000 201.91 3,845 0 377.03 4,907 0
84.03 600 1000 202.14 3,964 0 378.53 4,908 0
85.53 597 1000 202.41 4,071 0 380.03 4,908 0
87.03 595 1000 202.7 4,165 0 381.53 4,909 0
88.53 593 1000 203.03 4,246 0 383.03 4,909 0
90.03 591 1000 203.4 4,316 0 384.53 4,910 0
91.53 589 1000 203.81 4,375 0 386.03 4,910 0
93.03 587 1000 204.28 4,424 0 387.53 4,911 0
94.53 585 1000 204.8 4,464 0 389.03 4,911 0
96.03 583 1000 205.39 4,498 0 390.53 4,912 0
97.53 582 1000 206.04 4,527 0 392.03 4,912 0
99.03 580 1000 206.78 4,551 0 393.53 4,913 0
100.53 578 1000 207.61 4,572 0 395.03 4,913 0
102.03 576 1000 208.54 4,591 0 396.53 4,914 0
103.53 574 1000 209.58 4,608 0 398.03 4,914 0
105.03 573 1000 210.75 4,624 0 399.53 4,915 0
106.53 571 1000 212.06 4,640 0 400 4,915 0

Table 2: Simulated data used for validation example.

Reservoir parameters Value
Porosity (Φ) 0.1
Wellbore Radius (rw) - ft 0.3
Total Compressibility (Ct) - Psia-1 3.00E-06
Reservoir Height (h) - ft 30

Table 3: Assumed reservoir parameters for validation example.

Oil properties Value
Oil Formation Volume Factor (βo) – Res bbl/STB 1.2
Viscosity (µo) - cp 0.8
Bubble point Pressure (Pb) - psia 2000

Table 4: Assumed oil properties for validation example.

The well is diagnosed to be damaged (Positive skin of 9.4) is calculated from the pressure transient analysis module and then the well potential is calculated at (Pwf=zero) using this skin value and under ideal conditions using both productivity index equation and Vogel’s correlation to show the improvement that could be realized post a successful damage removal operation. All results are tabulated in Table 5.

Production parameters Value
Initial pressure (Pi) - psia 4997
Flow rate (Qo) – STB/D 1000
Production time (tp) - hrs 200

Table 5: Assumed production parameters for validation example.

In this case the software concludes that the well is candidate for matrix stimulation in case no mechanical damage is present and if economic feasibility is assured.

Figure 4 shows the input window of the developed software with all the reservoir, oil and production parameters. Figure 5 shows the semi-log representation of the pressure data for drawdown and build up analysis conducted by the software and Figure 6 shows the output of module one for pressure transient analysis of permeability, skin, and radius of investigation. All results are summarized in Table 6.

petroleum-environmental-biotechnology-simulated

Figure 5: Semi-log plot of simulated data for drawdown/build up analysis.

petroleum-environmental-biotechnology-analysis

Figure 6: Software output for drawdown/build up analysis.

Parameters Workflow
Developed software Sapphire Absolute error
Pressure transient analysis (Drawdown)
Skin Factor 9.53 9.89 2.70%
Permeability - md 18.82 19.2 1.90%
Pressure transient analysis (Build-up) 
Skin Factor 9.42 9.8 3.80%
Permeability - md 18.71 19.2 2.50%
Inflow performance relationship (Pre-treatment) 
Well Potential - Darcy Eq. STB/D 1220 1217 0.20%
Well Potential - Vogel's correlation STB/D 986 986 0%
Inflow performance relationship (Post-treatment) 
Well Potential - Darcy Eq. STB/D 2743 2740 0.10%
Well Potential - Vogel's correlation STB/D 2220 2220 0%

Table 6: Summary of outputs of sapphire and well-master for PTA and IPR.

While Figure 7 shows the input for the second module of the software which is used to construct the IPR curves. Figure 8 shows the software generated IPR curves using productivity index equation and Vogel’s correlation under ideal and current conditions. Production rates tabulated are estimated at an assumed flowing BHP of 1,500-psi

petroleum-environmental-biotechnology-feasibility

Figure 7: Inputs for treatment feasibility section.

petroleum-environmental-biotechnology-straight-line

Figure 8: Software generated IPRs (Vogel and straight-line).

The results of modules 1 and 2 are used to determine whether the well is candidate for matrix stimulation or not provided that economic feasibility is assured and no mechanical damage present and the output for this example is shown in Figure 9.

petroleum-environmental-biotechnology-advisor-results

Figure 9: Candidate selection advisor results.

Figures 10-13 Shows the execution steps using the Sapphire Ecrin module for pressure transient analysis and IPR curves using straight-line productivity index equation and Vogel’s correlation.

petroleum-environmental-biotechnology-sapphire

Figure 10: Input window for sapphire for PTA. Courtesy of Kappa.

petroleum-environmental-biotechnology-matching

Figure 11: Type curve matching using sapphire.

petroleum-environmental-biotechnology-case-right

Figure 12: Sapphire Vogel IPR (Ideal case-left – Damaged case-right).

petroleum-environmental-biotechnology-case-left

Figure 13: Sapphire Darcy law IPR (Ideal case-left – damaged case-right).

A tabulated summary of the results comparison between the developed software and the commercial software is given in Table 6 and visually presented in Figures 14 and 15.

petroleum-environmental-biotechnology-sapphire-output

Figure 14: Comparison between developed software and Sapphire output for PTA.

petroleum-environmental-biotechnology-developed-software

Figure 15: Comparison between developed software and Sapphire output for IPR.

Results and Discussion

A field application is discussed to prove the viability of this technology on performing pressure transient analysis and to show its advantages over conventional techniques (DHR on memory gauges)

The technology was tried in one well in shushufindi field in Ecuador [6,7], the main objective of the operation was to evaluate the well and perform pressure transient analysis for artificial lift design. Historically pressure data required for well test analysis was acquired by deploying memory gauges into the production tubing; however, some challenges were faced during well testing operations that encouraged the operator to try the fiber-optic telemetry enabled coiled tubing (FOETCT) for well testing [8]. Those problems were:

1. Ineffective closure caused by debris found in ball’s seat when using conventional shut-in method with memory gauges and standing valves.

2. Long test times due to wellbore storage effects and uncertainty about data completeness during well testing.

3. Operational issues related to slickline running of memory gauges.

To overcome the above challenges, a fiber-optic telemetry-enabled coiled tubing intervention was deployed to perform PTA in realtime. In this operation the down hole real-time pressure gauge will run in conjunction with an inflatable packer to minimize wellbore storage effects.

It was reported that the real-time monitoring of the downhole parameters with pressure and temperature sensors provided a positive confirmation of well inflow, reducing risk associated with running memory gauges. Additionally, it presented a safer way for artificial lift design to avoid non-productive time associated with waiting-on-equipment and extended rig operation [9].

The operational outline of the coiled tubing intervention was executed according to the below steps:

1. Drawdown testing was mainly conducted using N2 Lifting as a well kick-off method.

2. A mechanical tubing packer (Mechanical-set) and a double flapper check valve were used in the bottomhole assembly (BHA) to allow for downhole shut-in and prevent reservoir fluids from entering the CT workstring.

3. Real-time CCL measurement allowed for accurate placement of the BHA during well test analysis.

4. A surface acquisition system was used to receive the downhole measurements and allow well test engineers to perform well test interpretation in real-time

5. In summary, the use of this technology in shushufindi field in Ecuador [7] proved to provide matching results with conventional techniques. The well test analysis was made in less time due to real-time monitoring and the use of mechanical-packer that minimized wellbore storage effects. The ability to perform multiple applications in the same intervention, stimulation or logging for example greatly enhances the economics of the intervention operation.

Conclusion

Formation damage is one of the prominent reasons why oil and gas wells are not operating to their full capacity. Failure rate is also so high in executing stimulation jobs due to lack of following a structured approach in candidate selection and damage identification. Well test analysis is one of the key petroleum engineering aspects for quantifying damage and identifying candidates for stimulation; however, the conventional methods (DHR) used for PTA are ineffective and time consuming due to the risks associated with running memory gauges into completion nipples and the inability to read and optimize the test in real-time since all the data is being recorded downhole. Moreover, in case a well damage is identified, extra time will be needed to mobilize CT equipment for well stimulation.

This study tried to overcome all those hurdles by designing a new workflow that enables petroleum engineers to diagnose and treat the problem in the same run. This workflow relies mainly on pressure transient analysis (PTA) as a key technique for candidate selection through skin quantifications and inflow performance relationship analysis (IPR). The added values of the new workflow could be summarized in the below points:

1. The workflow avoided the drawbacks of conventional downhole recording (DHR) techniques for pressure data acquisition. As they frequently render inaccurate results or cause additional operational costs. So, the workflow relies on pressure data obtained by fiber-optic telemetry-enabled coiled tubing (FOTECT) as a communication medium for real-time well test analysis.

2. This workflow aims at adding a value of maximizing recovery levels using a streamlined study. This study combines the benefits of real-time well test analysis while leveraging the capability of the coiled tubing as a pumping medium to do perform stimulation job. So, diagnosis and remedial will be done with coiled tubing.

3. This new approach will save rig time due to optimized well test timing sequence. Also, save logistical time required to mobilize coiled tubing unit for remedial operation after identifying formation damage by combining both in the same run.

References

Author Info

Ahmed El-Attar*
 
Cairo University, Governorate 12613, Egypt
 

Citation: El-Attar A (2019) A Novel Workflow for Using Fiber-Optic Telemetry-Enabled Coiled Tubing in Candidate Selection. J Pet Environ Biotechnol. 10:398. doi: 10.35248/2157-7463.19.10.398

Received Date: Sep 20, 2019 / Accepted Date: Nov 22, 2019 / Published Date: Nov 29, 2019

Copyright: © 2019 El-Attar A, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Top