Introduction

Well C-15 is a horizontal production well with a drilling depth of 3095.00 m and a horizontal section of 914.76 m, resulting in a drilling rate of 57.04%. The hydraulic jet fracturing technique was utilized for the initial well reformation, which comprised of 12 fracturing stages. The well follows a five-spot well pattern method and has an early daily fluid production of 20.3 m3, stable to a daily oil production of 11.2 t, and the water content of 13.8%. This well corresponds to five injection wells and Fig. 1 illustrates the distribution of well location. The initial production level was high; however, the production curve declined gradually over time due to the reduction in fracture conductivity. The recovery rate remains low, and the potential of the well-controlled reserves has not been fully exploited. The average interval between each fracturing stage is 79.5 m, and the formation pressure in the well area maintains steady at 101.9%, indicating significant potential for increasing production. In the later period of development, the limit perforation method was utilized to optimize the potential position of 26 stages for densification. To assess the fracturing effect post fracturing and liquid production of each stage, a fracturing tracer monitoring operation was conducted. Table 1 displays the fundamental reservoir data of well C-15.

Fig. 1
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Well location distribution

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Table 1 Basic data of C-15 reservoir
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Tracer testing has been a vital tool in reservoir development research for decades. In the 1950s, it was primarily used to study fluid flow between oil and water wells in water flooding reservoirs (Wtkins and Mardock 1954; Flag et al. 1955). Brigham and Smith (1965) started quantitatively examining the inter-well flow characteristics of tracer using the five-point method, which is the theoretical basis for analyzing tracer test data. Cooke Jr (1971) proposed chromatography to determine the distribution of remaining oil by using distributed tracers with different solubility in oil and water phases. This theory expands the application of tracers in oil fields. Tang and Harker (1991) introduced a chromatographic conversion method to study remaining oil saturation using partitioned and non-partitioned tracers in the test. Subsequent research include single well tracer test and inter-well tracer test (Bu et al. 2015; Khaledialidusti et al. 2017). Murayri (2019) described a single well chemical tracer test conducted in Sabriyah and Raudhatain fields and Sanni (2018) introduced a simultaneous test of a distributed tracer and a non-distributed tracer, which can provide better descriptions of the reservoir. The application of tracer testing technology is not limited to reservoirs but also used in gas reservoirs (Sanni et al. 2017; Patidar et al. 2022). Shook (2003) proposed the residence time distribution method to estimate the flow geometry of fractured geothermal reservoirs, which was further studied in subsequent work (Shook and Forsman 2005; Shook 2017). Other scholars have also employed this method to interpret tracer test data (Viig et al. 2013; Tiong-Hui et al. 2018; Al-Qasim et al. 2020). The residence time distribution method has been proven to be a reliable method for interpreting tracer test data.

In recent years, research on the development of tight oil and shale gas has become a major focus in the petroleum industry (Wang et al. 2016; Hou et al. 2021; Song et al. 2020; Li et al. 2019; Sohail et al. 2022; Radwan et al. 2022). As a result, researchers in the oil industry have conducted extensive studies on the seepage mechanism of horizontal wells (Zhao et al. 2021; Xia et al. 2021; Zhang et al. 2022). The development of unconventional oil and gas resources requires the use of hydraulic fracturing and other technologies (Li et al. 2018; Singh et al. 2020; Vishkai and Gates et al. 2019; Hakimi et al. 2023; Zhao et al. 2022; Baouche et al. 2022). Spencer et al. (2013) reported on two horizontal tracer tests conducted on horizontal wells in the Eagle Ford Shale, providing insight into tracer response and the current state of hydrocarbon-based tracer technology for hydraulic fracturing. Li et al. (2017) developed a simulation tool for tracer injection and backflow processes in each stage of fractured horizontal wells. They proposed a parameter to describe the relationship between the secondary fracture network and tracer backflow profile shape. Kumar et al. (2020) proposed a comprehensive approach based on tracer and pressure interference data analysis to determine the interference levels between multi-well fractured horizontal wells. Liu et al. (2022) developed an efficient numerical model based on an embedded discrete fracture model (EDMM) to quantify tracer flowback behavior and describe fracture networks. To develop tight oil reservoirs, horizontal wells and fracturing operations are usually combined. Fracturing construction changes the reservoir, forming a complex fracture network, increasing the seepage volume of the reservoir, and ultimately improving productivity. To gain a clear understanding of the reservoir heterogeneity after fracturing and the productivity contribution of each corresponding fracturing stage, tracer tests are performed on target well. The main principle of using tracer to monitor staged fracturing horizontal wells is to select different types for different fracturing stages. Tracers with the same concentration are added to the fracturing fluid during the fracturing process, and then, the tracer concentration curve changes with time as flowback fluid is sampled, analyzed, and processed. The obtained test data are used for interpretation, and when combined with the production data of each fracturing stage, the fracturing effect is evaluated. Among the tracers used to monitor fractured horizontal wells, rare earth elements of the same series are often used as representatives. Metal chloride, EDTA·2Na, DTPA, sodium hydroxide, and other substances are used to form stable metal complex solution to prepare tracers.

In comparison with chemical tracer, radioisotope tracer, and stable isotope tracer, the micro-substance tracer used in this test has several advantages:

  1. (1)

    It requires a smaller dose and is less expensive;

  2. (2)

    It is not harmful to the surrounding environment and operators;

  3. (3)

    It is easy to operate;

  4. (4)

    It is highly stable and is not easily adsorbed by the formation;

  5. (5)

    It is easy to detect in production wells.

In previous studies related to tracer testing, chemical and radioactive tracers were commonly employed, with vertical well testing being the predominant method used. However, there have been limited investigations utilizing tracer tests to measure the production profile of fractured horizontal wells and evaluate the effectiveness of hydraulic fracturing. Therefore, this paper presents a comprehensive study on the testing of micro-substance tracers for horizontal wells with multiple fracturing stages. The testing period was divided into two phases: the fracturing fluid flowback period and the stable production period, allowing for separate analysis of the characteristics of the production fluid profile. The residence time distribution interpretation method was utilized in the tracer test of multi-stage fractured horizontal wells. By integrating heterogeneity information obtained from the tracer test with well production performance data, the effectiveness of hydraulic fracturing was evaluated. However, it is important to note that increasing the number of stages also increases the number of different tracers required, placing greater demands on tracer selection. Additionally, a shorter tracer test cycle may negatively impact the accuracy of test results.

Methodology

Tracer test scheme design

For the purpose of multi-level monitoring, a total of 18 distinct tracers were utilized in this test. Each tracer is non-toxic, non-polluting, non-radioactive, is not reactive with either other tracers or formation fluids. The background concentration in the testing area was below 0.1 ppb. To achieve the monitoring objective and prevent the dilution of the fluid concentration from unmonitored layers to the target layer, the tracer's designed concentration was raised to 100 ppb. The dosage of each tracer stage was designed to be 1/10,000th of the volume of fracturing fluid (pre fluid + sand carrier fluid) introduced in each stage. This ensured that the concentration of each tracer stage remained consistent after injection. The specific tracer dosage information for each stage is listed in Table 2.

Table 2 The dosage of tracer in each stage of well C-15
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Sampling and testing

It was ensured that the tracer would be detectable during any flowback period when fracturing fluid flowback occurs. Various sampling frequencies were set for distinct flowback periods. The flowback period is determined mainly based on fracturing construction design. The tracer is injected into the formation along with the fracturing fluid during the fracturing process. After the completion of fracturing, the well will be shut down and stewed, and the liquid will be discharged. The tracer is injected in the whole process of the pre-injection and carrier injection periods, so that the tracer can be detected whenever there is fracturing fluid flowback. In the initial stage, the return displacement is large, so the sampling frequency is increased.

Details of the specific sampling design are displayed in Table 3.

Table 3 Well C-15 tracer test sampling design
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From October 25, 2021, to August 05, 2022, the sampling process was carried out for 285 days, during which 916 bottles were collected. The wellhead produced liquid samples were initially separated into oil and water, and their concentration of effective tracers was determined through high-resolution ICP-mass spectrometry. To verify any anomalies in the test results, suspicious data points were either retested or encrypted based on the tracer's output. In total, 2109 samples were tested.

Results and discussion

Analysis of primary liquid producing stage

The sampling data underwent analysis to eliminate any interference from abnormal data. The tracer production began after October 25, 2021. At the commencement of sampling, stage 13 displayed the highest concentration, peaking at 12.18 ng/mL. Out of the 18 monitoring stages, peak concentration varied from 0.11 to 63.53 ng/mL, with the lowest being stage 16 and the highest being stage 24. The peak time range was between October 25, 2021, and December 28, 2021, with the 18th stage having the earliest peak and the third stage having the latest. Figure 2 illustrates the tracer output for each stage.

Fig. 2
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Tracer production in each stage of well C-15

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After analyzing the tracer output for each stage, it can be determined that the main concentration of tracers was produced between October 25, 2021, and December 12, 2021. Subsequent monitoring showed that only stages 13 and 24 had low concentrations of output on February 19, 2022, while the output concentration of tracer in different stages varied significantly.

Hence, it can be concluded that the primary output of tracers occurred during a specific period after the beginning of sampling, and the subsequent output was relatively stable. To aid in data interpretation, the production stage of horizontal wells was divided into two categories, namely, the fracturing fluid flowback period and the stable production period, with December 12, 2021, being the cut-off point.

Moreover, it should be noted that for the same injection concentration, a proportional relationship exists between the liquid production and the tracer concentration. Therefore, the liquid production and contribution rate were calculated for each stage, which helped in determining the primary liquid production stage.

The formula used to calculate the liquid production in each stage is as follows:

$${q}_{i}=q\frac{{c}_{i}}{\sum c}$$
(1)

\({q}_{i}\) represents the liquid production of any given stage, \(q\) denotes the total liquid production of the well, \({c}_{i}\) represents the tracer output concentration of any given stage, \(\sum c\) represents the sum of tracer output concentration for each stage.

Fracturing fluid flowback period

During this period, the tracer was continuously produced and had a high concentration. Using tracer flowback data, the amount of liquid produced in each stage was calculated and Fig. 3 displays the results. It can be observed that the13th stage had the highest liquid production, with a volume of 112.96 m3, while the 16th stage had the lowest liquid production, with a volume of 0.72 m3. The production capacity of the different stages varied greatly, indicating that the fracturing fluid flowback period was not uniform throughout all the stages of horizontal wells.

Fig. 3
figure 3

Liquid production of each stage during flowback period

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Figure 4 displays the contribution rate of liquid production for each stage. The primary production stages are 4, 6, 13, 22 and 24, based on their respective contribution rates. The contribution rate of liquid production was highest for stages 13 and 24, with both stages reaching or exceeding a contribution rate of 20%. Meanwhile, the contribution rate for stage 4 was 18%, and stages 6 and 22 showed a contribution rate exceeded 5%. For all other stages, the contribution rate of liquid production did not surpass 5%. Figure 4 provides a visual representation of the contribution rates for liquid production in each stage, highlighting the significant production stages and their respective contribution rates, which can be crucial for further analysis of the well’s production performance.

Fig. 4
figure 4

Contribution rate of liquid production in each stage during flowback period

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Stable production period

During this period, there was a significant decrease in tracer output as compared to the initial period, and it remained at low levels throughout. Figure 5 illustrates the liquid production of each stage during this period. The highest liquid production was observed in stage 24, with a volume of 124.28 m3, whereas stage 7 had the lowest liquid production, with a volume of 13.68 m3. The production capacity of each stage continued to vary greatly during this period.

Fig. 5
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Liquid production in each stage during stable production period

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Figure 6 displays the contribution rate of liquid production in each stage. The primary production stages during this period were identified as 3, 4, 6, 13, 19 and 24. Out of these, stage 3 had a contribution rate of 8%, while the other stages, namely the contribution rate of stages 4, 6, 13, 19 and 24, all had a contribution rate exceeding 10%. However, the contribution rate of other liquid production stage did not exceed 5%.

Fig. 6
figure 6

Contribution rate of liquid production for each stage during stable production period

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In contrast to the flowback period, the tracer observation concentration remained low throughout the stable production period. Furthermore, the gap between the liquid production in primary and other stages was less compared to the flowback period.

Table 4 demonstrates that the liquid contribution rate of a specific stage during different production periods changes with the alteration of production pressure difference. In the stable production period, the contribution rate of liquid production for stage 6 increased, whereas other primary liquid producing stages exhibited a decline to varying degrees. Additionally, the contribution rate of stage 3 and 19 increased and became the primary liquid producing stage during this period. Accurately identifying the primary liquid producing stage can assist reservoir engineers in formulating more effective production and development plans, thereby enhancing the productivity the reservoir.

Table 4 Comparison of liquid contribution rate between flowback period and stable production period
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The tracer output curves in the primary liquid production stage are shown in Fig. 7.

Fig. 7
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Test curves of primary liquid production stage

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The tracer concentration curves of stage 23 and 21 are shown in Fig. 8.

Fig. 8
figure 8

Test curves of stages 23 and 21

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Upon examining Figs. 7 and 8, it is evident that the tracer exhibited a higher concentration of output during the initial monitoring period. The shape of the tracer curve is multi-peaked, indicating variations in liquid production rate throughout the well. Among the primary liquid producing stages, stages 4 and 6 attained their peak concentration very quickly. In contrast, stages 13 and 24 reached their peak concentration after a significant time lag. Different from the other liquid producing stage, the tracer continuous producing time of each primary liquid producing stage was significantly longer. This implies that the primary liquid producing stages contribute consistently to the liquid production rate over an extended period. Moreover, the peak concentrations in stages 4, 13 and 24 were several times to dozens of times higher than that of the other liquid production stage, which highlights their significance in controlling the overall liquid production rate.

To facilitate a more intuitive comparative analysis, the tracer output curves of stage 21 and 23 were compared with the average concentration of the tracer output across all stages. Figure 9 demonstrates that the tracer output concentration in the other liquid production stage is usually lower than the average concentration of tracer output in all stages.

Fig. 9
figure 9

Comparison of stages 21 and 23 test curves and average concentration

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The primary production stages of well C-15 have experienced changes across different production periods, primarily due to variations in the pressure environment as it transitions from the flowback period to the stable production period. Various production stages are responsive to fluctuations in the pressure environment, resulting in shifts in the main production stages. Gaining insight into these changes enables the staff to make necessary adjustments to the production plan.

In this test, tracer production was visibly detected in all fracture stages of well C-15. By evaluating the productivity contribution of each fracturing stage based on the tracer production, the intended objective of tracer detection was successfully achieved. The test data indicates a significant increase in liquid production contribution in stages 3, 4, 6, 13, 19, 22, and 24. This signifies that the artificially generated fractures formed during the fracturing process possess notable flow capacity. As a result, it can be inferred that the fracturing operation has been successful in enhancing production, as intended.

The residence time distribution of the test data

The residence time distribution analysis method has proven to a useful tool in interpreting tracer test data. Shook et al. (2003) were the first to propose this approach, which uses tracer test data to evaluate to evaluate reservoir heterogeneity. The tracer flowback concentration curve can be treated as a tracer concentration distribution function that varies with time. Initially, we normalized the tracer concentration \(C(t)\) to obtain the time distribution function \(E(t)\) (Eq. 2).

$$E(t) = \frac{C(t) \cdot Q}{M}$$
(2)

The tracer detection concentration is typically expressed as a volume or mass fraction, where \(Q\) represents the daily water injection rate and \(M\) represents the total mass of injected tracer. In a closed system, the principle of mass conservation stipulates that Eq. (3) is satisfied.

$$\sum\limits_{j} {\int_{0}^{\infty } {E_{j} (t){\text{d}}t = 1} }$$
(3)

In the residence time distribution analysis method, the zero-order moment \(m_{0}\) (Eq. 4) and the first-order moment \(m_{1}\) (Eq. 5) are often employed the zero-order moment represents the relative production of tracer in the corresponding production wells.

$$m_{0} = \int\limits_{0}^{\infty } {E(t){\text{d}}t}$$
(4)
$$m_{1} = \int\limits_{0}^{\infty } {tE(t){\text{d}}t}$$
(5)

The average residence time of tracer \(\tau\) was obtained by normalizing the first moment \(m_{1}\) to zero moment \(m_{0}\).

$$\tau = \frac{{m_{1} }}{{m_{0} }}$$
(6)

Shook (2003) and Shook and Forsman (2005) proposed using residence time distribution to capture reservoir flow and geometric characteristics by characterizing the reservoir's flow capacity and storage capacity using \(F(t)\)(Eq. 7) and \(\Phi (t)\)(Eq. 8).

$$F(t) = \frac{{\int\limits_{0}^{t} {E(\tau )} {\text{d}}\tau }}{{\int\limits_{0}^{\infty } {E(t){\text{d}}t} }}$$
(7)
$$\Phi (t) = \frac{{\int\limits_{0}^{t} {\tau E(\tau )} {\text{d}}\tau }}{{\int\limits_{0}^{\infty } {tE(t){\text{d}}t} }}$$
(8)

Combine \(t\) and \(\Phi (t)\) to draw \(F\)–\(\Phi\) chart to characterize the heterogeneity of reservoir. The heterogeneity of reservoir can be expressed by Lorentz coefficient \(L_{{\text{c}}}\) (Eq. 9):

$$L_{c} = 2 \cdot \left\{ {\int\limits_{0}^{1} {Fd\Phi - 0.5} } \right\}$$
(9)

The data from the chosen fractured stage underwent processing and interpretation, and the resultant calculation are presented in Table 5.

Table 5 Residence time analysis results of primary liquid production stages
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For stage 4, the relative output of tracer \(m_{0}\) was 313.64, with an average residence time \(\tau\) of 37.81 days and a Lorentz coefficient \(L_{{\text{c}}}\) of 0.59. In stage 6, the relative output of tracer \(m_{0}\) was 150.26, with an average residence time \(\tau\) of 37.81 days and a Lorentz coefficient \(L_{{\text{c}}}\) of 0.59. For stage 13, the relative output of tracer \(m_{0}\) was 456.08, with an average residence time \(\tau\) of 40.93 days and a Lorentz coefficient \(L_{{\text{c}}}\) of 0.47. Lastly, in stage 24, the relative output of tracer \(m_{0}\) was 400.95, with an average residence time \(\tau\) of 41.80 days and a Lorentz coefficient \(L_{{\text{c}}}\) of 0.46.

In the four primary liquid production stages, the contribution rate of liquid production from highest to lowest is q13 > q24 > q4 > q6. When using the residence time distribution method to calculate the relative tracer yield, m13 > m24 > m4 > m6, yielding consistent results. Compared to non-primary liquid producing stage, the relative tracer yield in the primary liquid producing stage is noticeably higher.

The degree of heterogeneity of fracture stages

The heterogeneity of each stage is shown by the \(F\)–\(\Phi\) curve. The corresponding curves are shown in Figs. 10 and 11.

Fig. 10
figure 10

The degree of heterogeneity of the primary liquid producing stages

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Fig. 11
figure 11

The degree of heterogeneity of stages 23 and 21

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The \(F\)–\(\Phi\) curve features the diagonal line as the representation of a homogeneous fracture network, while the degree of deviation from this line illustrates the heterogeneity of the fracture network. The value of \(L_{{\text{c}}}\) indicates the strength of the fracture network’s heterogeneity. Based on the calculation of the Lorentz coefficient, it is evident that both the primary liquid producing stages and the non-primary liquid producing stages exhibit considerable heterogeneity. In the four primary liquid producing stages, stage 6 displays the highest degree of inhomogeneity with a \(L_{{\text{c}}}\) of 0.61, while stage 24 exhibits the weakest degree of heterogeneity with a \(L_{{\text{c}}}\) value of 0.46.

The derivative curve reflects the uniformity of fracture network. It can be seen from Fig. 12 that the fracture network formed at the position of the stage 6 is relatively uniform. The derivative curve of the stage 24 has an obvious sudden change, rapid decline in the early, mid changes slowly in nearly horizontal stage, the late slow decline. This indicates that the fracture network in this area is complex. Further verification can be considered in combination with other data such as micro-seismic test data.

Fig. 12
figure 12

The degree of heterogeneity curves in logarithmic coordinates

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Production dynamic data of test well

Production of well C-15 before and after fracturing is shown in Fig. 13.

Fig. 13
figure 13

Production dynamics of well C-15

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The production of C-15 well had significantly decreased before the fracturing process with low productivity over a period of time. However, after the fracturing reconstruction construction, the productivity markedly improved compared to its pre-reconstruction performance, indicating a successful reconstruction process. The tracer test results further support that the fracturing process yielded a positive effect.

Conclusions

  1. (1)

    The tracer selected for this test belongs to the same series of rare earth elements, exhibiting comparable properties. With a diverse range of types accessible, they can be readily distinguished. This choice effectively meets the monitoring requirements for multi-stage fractured horizontal wells in the designated test area.

  2. (2)

    The main production section during the flowback period may undergo changes in the stable production period. When identifying the main production stage, it is crucial to consider the production contributions from different periods and discern any variations.

  3. (3)

    The information gathered from the tracer test, encompassing details about reservoir heterogeneity and fluid production in each fracture stage, can be synergistically analyzed alongside the production dynamic data of the test well. This comprehensive assessment allows for an evaluation of the effectiveness of the fracturing process.