A New Method for Determining Production Profiles Based on Intelligent Slow-Release Chemical Tracers

Abstract

With the development of tracer technology and the improvement of fine management in oil fields, chemical tracer monitoring is widely used to analyze the production profiles in commingled wells and horizontal wells. However, most existing tracer technologies can only determine the production profile and cannot calculate the water cut. This paper proposes an intelligent slow-release chemical tracer monitoring technology and a corresponding interpretation methodology, which can quantify the oil and water production rates and dynamically analyze the water cut of production profiles by simultaneous deployment of oil-soluble and water-soluble tracers. To validate this approach, this method was applied to well A of the Bohai Oilfield. The results showed that the calculation model based on produced tracer concentration can quantitatively determine the production profile and water cut of the monitored well. During the stable production period, Well A exhibited high production rates and a low water cut, and the contribution of oil production varied greatly among different layers. The first and third sections were identified as the main contributors, accounting for 51.8% and 23.2% of production, respectively, while the second and fourth sections showed lower contributions of 15.1% and 9.9%. The water cut of each section was below 30%. This intelligent slow-release tracer monitoring technology allowed for continuous production profiles in the monitored well. The proposed method provides effective guidance for characterizing the production profile and water flooding patterns of each layer. It is helpful for the efficient development of oil and gas reservoirs.

1. Introduction

In oilfield development practices, the simultaneous exploitation of multiple reservoirs through commingled production wells is often constrained by geological complexities and engineering limitations. During the production of a well, individual layer performance is significantly affected by inter-layer interference, leading to substantial variations in production across layers and resulting in poor recovery efficiency in certain intervals. Furthermore, due to reservoir heterogeneity, some layers experience premature water breakthrough, which not only increases water production in the well but also reduces oil recovery. Recent advancements in inflow control technologies have highlighted the critical importance of monitoring individual layer production performance. Accurate assessment of zonal contributions provides valuable insights for reservoir dynamic analysis and production optimization strategies in commingled wells. Currently, the primary methods for determining production profiles of commingled wells include production profile logging, distributed optical fiber temperature testing, and tracer monitoring.

Production profile logging is a diagnostic technique aimed at assessing fluid production distribution across reservoir intervals during oil and gas well production [1]. Currently, the main methods include impedance-based production logging, array probe production logging, low-productivity production logging, and radioactive three-phase flow logging [2,3]. Impedance-based production logging integrates turbine flowmeters with capacitive water-cut sensors. This method determines liquid production profile through volumetric flow measurements and water-cut quantification via dielectric properties, which is suitable for water-cut testing in high water cut wells [4]. Recent advancements in sensor configurations have enhanced its reliability in high-water-cut wells. Array probe-based production logging employs electrical conductivity sensors to characterize localized water-holding patterns. It can measure local water holding capacity by electrical conductivity sensors and establish empirical correlations between water cut and water-holding capacity to determine the water cut, which is easy to operate and convenient to maintain. This technology is applicable to oil wells with water cut exceeding 50% [5,6]. For low-productivity wells, specialized instrumentation utilizing multi-electrode arrays has been developed. These systems measure the time difference in oil–water separation interface separation to calculate the flow of oil and water phases and determine the flow and water cut of each layer [7]. Radioactive three-phase flow logging utilizes low-energy X-ray absorption technology, which applies the radiation source characteristics to determine the average density and water cut of multiple phase fluids [8]. The above production logging technologies are the main methods currently used for production profile testing. In recent years, some new production logging techniques have also emerged. Hao et al. has developed a dual-receiver fiber optical probe array multiphase logging tool for three-phase flow monitoring [9]. To address production monitoring challenges in horizontal and hydraulically fractured wells, researchers have adopted electrical resistance probes arrays and integrated systems combining electromagnetic flowmeters with differential pressure sensors. These methodologies enable a comprehensive determination of fluid production profiles under complex downhole conditions [10,11]. While these technologies can provide valuable production profile and water cut measurements, they exhibit two critical limitations. First, these techniques cannot achieve continuous monitoring of target wells. Second, their implementations require the production string to be pulled up and down during the testing process, which is complex and expensive.

Distributed fiber optic temperature sensing (DTS) enables real-time thermal mapping of wellbore environments through dynamic temperature monitoring. This technology provides continuous diagnostic capabilities for inferring production profiles via advanced interpretation of temperature-derived fluid phase distribution. Recent advancements in fiber optic measurement and intelligent well completion technologies have driven the emergence of novel downhole temperature monitoring systems, with DTS demonstrating particular prominence [12,13]. Scholderle et al. obtained the wellbore temperature distribution of injection wells in the Southern German Molasse Basin based on distributed fiber optic temperature monitoring data and determined the positions of the main water-absorbing layers [14]. Based on distributed temperature sensing data, some scholars use data inversion methods to obtain wellbore flow distribution and formation permeability, which deepens the understanding of reservoir characteristics [15,16,17]. Huang et al. developed an innovative multiphase wellbore thermal model, integrating the sequential quadratic programming legacy and particle swarm optimization algorithms. This computational method enables quantitative characterization of liquid production profiles and water cut dynamics through DTS data inversion [18]. Arief summarized the inversion methods of distributed temperature optical fiber monitoring and analyzed the advantages and disadvantages of sound speed estimation methods, Joule–Thomson coefficient, and machine learning methods (convolutional neural network, support vector machine, ensemble Kalman filter algorithm) which provide the development trend in distributed optical fiber monitoring data interpretation methods [19]. Furthermore, distributed fiber optic temperature sensing has proven instrumental across diverse operational domains, including real-time wellbore integrity diagnostics and hydraulic fracturing performance evaluation through continuous thermal gradient analysis [20,21]. Due to the complexity of wellbore and fluid conditions, the large amount of monitoring data, and the diversity of data interpretation methods in current monitoring, current interpretation software is still in the early application stage. The interpretation error is relatively large when multi-phase flow occurs in the wellbore, which makes it difficult to process actual monitoring data and limits the application of this technology.

Tracer monitoring has emerged as an effective dynamic monitoring technology, which is widely used in interwell connectivity evaluation, hydraulic fracturing diagnostics, and production profile characterization. In interwell connectivity assessment, tracer monitoring can evaluate the degree of interwell connection and calculate the physical property parameters of a related reservoir, which is based on the production of tracers and reservoir parameters [22,23,24]. Stephania et al. systematically demonstrated the technical superiority of carbon quantum dot (CQD) tracers for interwell connectivity assessment through field applications [25]. In hydraulic fracturing diagnostics, inverse modeling of tracer transport dynamics enables fracture parameter quantification, which can achieve critical insights into stimulation effectiveness [26,27,28]. For both non-clustered and clustered fracturing wells, Abu conducted comprehensive evaluations using tracer technology to assess production profiles, fracturing effectiveness and production impacts and ultimately optimizing volumetric fracturing techniques [29]. Liu developed an embedded discrete fracture model to analyze the quantitative correspondence between tracer backflow characteristics and fracture network morphology in fractured wells, and they verified the accuracy of this model by comparing it with microseismic data [30]. In production profile characterization, a large number of studies have been conducted on water production point monitoring and production profile monitoring [31,32,33]. Alexey presented an application of intelligent inflow tracer technology for the liquid production profile of horizontal wells. While this work addressed the construction methodology and operational testing principles, it did not elaborate on the critical interpretation procedures of unique oil- and water-soluble tracers [34]. Li systematically investigated the functional mechanism of slow-release chemical tracers and developed three types of water-soluble chemical tracers. Through controlled laboratory experiments, the study quantified the effect of tracer components, temperature, fluid salinity, and flow velocity on the dissolution of tracers [35]. These tracers were successfully applied in monitoring the water production profile of horizontal wells and demonstrated a robust capability to identify the water-flooded layer within heterogeneous reservoirs. Xu presented a comprehensive application of long-acting, slow-release tracers in the Bohai Oilfield, delineating their distinctive advantages in the dynamic production monitoring of oil wells [36]. If the tracer type is not compatible with the reservoir, it may lead to reservoir damage, and some scholars have also studied its pollution mechanism [37,38]. The above studies have made great contributions to determining interwell connectivity, hydraulic fracturing diagnostics, and production profile characterization using tracer monitoring. With the increasing number of high water-cut reservoirs, the demand is increasing for the determination of layer-specific production allocation and water cut quantification through tracer monitoring. And it is necessary to develop robust interpretation methodologies that can systematically determine layer production contribution and water cut.

This paper develops an interpretation model of an oil–water two-phase intelligent slow-release tracer that can identify oil–water profiles and quantify the water cut in individual layers. This model innovatively integrates the release kinetics of smart tracers, multiphase flow dynamics in wellbores, and tracer dissolution mechanisms. Field implementation in Well M demonstrates the model’s effectiveness in determining production profiles and water cut distributions across monitored intervals. These results provide valuable operational guidelines for implementing intelligent tracer systems and are of certain guiding significance for optimizing reservoir dynamic management.

2. Interpretation Method for Intelligent Slow-Release Chemical Tracers

With advancements in chemical engineering and the evolution of the petroleum industry, tracer monitoring technology has found growing applications in oilfields. Particularly in complex reservoirs, this technology demonstrates distinctive advantages such as operational adaptability, environmental compatibility, and streamlined processes. It has achieved production profile testing without shutting down production in wells. Due to the need for the long-term monitoring of water cut oil wells, intelligent slow-release tracers have been progressively developed and refined. At present, intelligent slow-release tracers have been widely used in the petroleum industry. Intelligent slow-release tracers are synthetic chemical materials with good solubility, low toxicity, environmental friendliness, wide raw material sources, and low cost. They can be engineered into oil-soluble, water-soluble, or gas-soluble forms by combining with hydrophilic or lipophilic slow-release matrices. Water phase tracers can selectively release diagnostic compounds into water to indicate the water production of layers, while oil phase tracers can only release tracers into the oil to mark the oil production of layers. The same type of tracers have the same chemical properties and solvability, but some molecules have been labeled to identify their differences. Due to the need to monitor multiple layers and types of fluids simultaneously, intelligent slow-release tracers are generally composed of chemically synthesized substances. The selection process of intelligent slow-release tracers should be comprehensively determined based on reservoir characteristics, fluid types, and monitoring environment. During tracer monitoring, intelligent slow-release tracers are processed into strips, and different types of water phase tracers and oil phase tracers are fixed in a certain order outside the short circuit of a screen tube, which are arranged on the corresponding production string. A packer is installed between the short circuit to isolate fluid flow, which can achieve continuous monitoring of the production status of layers corresponding to the short circuit (Figure 1). Due to the unique tracer signature assigned to each monitoring interval, the fluid samples produced are continuously collected during monitoring operations. Through advanced chemical analysis techniques, the concentration profiles of individual tracers are quantitatively determined. These tracer-specific concentration datasets enable intelligent identification of both production profile and water cut through integrated data interpretation algorithms. In commingled wells, heterogeneous reservoir properties and fluid phase behavior lead to differential layer productivity. Since each short circuit and tracer corresponds to a section of reservoir, the production status of such tracers can characterize the fluid production status of this reservoir.

Within a closed-boundary reservoir, a production well operates under single-phase fluid flow conditions obeying Darcy’s law. The production of water-specific layers can be mathematically expressed as follows [39]:

$$Q_{wi}={\displaystyle \frac{0.5358K_{wi}{h}_i}{B_w\left(ln\frac{r_{ei}}{r_w}-0.5+S\right)}}$$

(1)

In the formula, Q_wi is the daily water production in the i-th layer, m³/d. K_wi is the water phase permeability in the i-th layer, mD. h_i is the thickness of the i-th layer, m. B_w is the volume coefficient of water, m³/m³. r_ei is the outer boundary radius of the i-th layer, m. r_w is the wellbore radius, m. S is the skin factor.

Similarly, the oil production of a layer can be written as follows:

$$Q_{oi}={\displaystyle \frac{0.5358K_{oi}{h}_i}{B_o\left(ln\frac{r_{ei}}{r_w}-0.5+S\right)}}$$

(2)

where Q_oi is the daily oil production in the i-th layer, m³/d. K_oi is the oil permeability in the i-th layer, mD. B_o is the volume coefficient of oil, m³/m³.

Within the production tubing, fluid phases exhibit unidirectional flow characteristics. If the fluid flows steadily, due to the same flow state of the oil and water phases, the flow velocity is divided by the tubing’s cross-sectional area, which can be expressed as follows:

$$v_i={\displaystyle \frac{{{\displaystyle \sum }}_{j=1}^i{Q}_j}{\pi r_b^2}}$$

(3)

where v_i is the flow velocity of the oil or water phase in the i-th short circuit, m/s. Q_j is the production of the oil or water phase in the j-th layer, m³/d. r_b is the inner radius of short circuiting, cm.

When the fluid in the slotted pipe contacts the tracer, the lipophilic tracer dissolves in the oil phase, the hydrophilic tracer dissolves in the water phase, and they enter the tubing. When the tracer flows in the tubing, its concentration equation can be expressed as follows:

$${\displaystyle \frac{\partial C}{\partial t}}+v{\displaystyle \frac{\partial C}{\partial x}}=\sigma {\displaystyle \frac{{\partial }^{2}C}{\partial x^2}}$$

(4)

where C is the tracer concentration, μg/L. t is time, s. v is the flow velocity of the oil or water phase at a certain position of tubing, m/s. x is the distance in the direction of the oil pipe, m. $\sigma$ is the diffusion coefficient of the tracer in the fluid.

When the intelligent tracer is released into the fluid in the slotted pipe, according to the indoor experimental evaluation results of the tracer, it can be concluded that the tracer release concentration is related to factors such as release time and flow rate. Considering the differences in tracer release concentration caused by flow rate and time in each section, the tracer concentration measured based on the samples needs to be corrected. This correction can be used to compensate for the test concentration in order to more accurately obtain the liquid production of each section. According to the characteristics of slow-release tracers, the concentration expression can be written as follows (Xu et al., 2024 [36]):

$$C=C_s+C_0{e}^{-kQt}$$

(5)

where C represents the released concentration of tracer during contact with the fluid, μg/L. C_s is the tracer concentration in the fluid under steady conditions, μg/L. C_o is the initial concentration, μg/L. k is the correlation coefficient, according to the experimental result. Q is the production, m³/d. t is time, d.

Due to the dissolution of the tracer in the slotted pipe before entering the oil pipe, the tracer reaches a stable state of dissolution in the slotted pipe. Therefore, the production of tracers in the layer is equal to the production in the entire wellbore. In multi-layer commingled production scenarios, the total oil production rate and water production rate equate to the summation of individual layer contributions. Assuming N layers of oil well are produced, the oil well production can be expressed as follows:

$$Q_o={{\displaystyle \sum }}_{j=1}^N{Q}_{oj}$$

(6)

where Q_o is the daily oil production of the well, m³/d. Q_oj is the daily oil production in the j-th layer, m³/d. N is the number of commingled production layers in the well.

In a well, the production of a certain tracer is the multiplication of fluid production and the concentration, which can be expressed as follows:

$$m_i=Q{C}_i$$

(7)

where m_i is the tracer production of the i-th layer, g. C_i is the tracer production concentration of the i-th layer, μg/L.

The total production of tracers is the sum of various tracers’ production, which can be expressed as follows:

$$m={{\displaystyle \sum }}_{j=1}^N{m}_j=Q{{\displaystyle \sum }}_{j=1}^N{C}_j$$

(8)

The intelligent slow-release tracer system is composed of a polymer material skeleton integrated with diagnostic tracers. The polymer material has thermal stability and saline resistance. This polymer matrix is the carrier of the tracer, which controls the dissolution rate of the tracer. To ensure comparability of monitoring data, the same polymer material skeleton and tracer with similar properties are generally used during the monitoring of the same well, and the tracer dissolution rate is the same. The production of tracer is directly proportional to the liquid production in each layer, and the proportion of tracer production in each layer is equal to the ratio of tracer production in that layer to the total tracer production, which can be expressed as follows:

$${\eta }_i={\displaystyle \frac{Q_i}{Q}}={\displaystyle \frac{m_i}{m}}={\displaystyle \frac{Q{C}_i}{Q{{\displaystyle \sum }}_{j=1}^N{C}_j}}={\displaystyle \frac{C_i}{{{\displaystyle \sum }}_{j=1}^N{C}_j}}$$

(9)

where ${\eta }_i$ is the percentage of liquid produced in the i-th layer, %.

Due to the single sensitivity of tracers, oil-soluble tracers release tracer substances in contact with the oil phase, while water-soluble tracers release tracer substances in contact with the water phase. When oil and water are produced simultaneously in the well, both oil-soluble and water-soluble tracers are installed in each layer of the slotted pipe. Based on the fluid produced at the wellhead under stable production conditions, oil and water samples are collected separately to monitor the concentration of various tracers. According to the above method, the oil and water production of each layer can be obtained. Due to the water cut of the layer being equal to the ratio of its water production to its liquid production, the water cut of layer can be expressed as follows:

$$f_{wi}={\displaystyle \frac{Q_{wi}}{Q_{oi}+Q_{wi}}}={\displaystyle \frac{\frac{C_{wi}}{{{\displaystyle \sum }}_{j=1}^N{C}_{wj}}}{\frac{C_{wi}}{{{\displaystyle \sum }}_{j=1}^N{C}_{wj}}+\frac{C_{oi}}{{{\displaystyle \sum }}_{j=1}^N{C}_{oj}}}}$$

(10)

where f_wi is the water cut of the i-th layer, %. Q_wi is the daily water production of the i-th layer, m³/d. Q_oi is the daily oil production of the i-th layer, m³/d. C_wi is the concentration of the water-phase tracer produced in the i-th layer, μg/L; C_oi is the concentration of the oil-phase tracer produced in the i-th layer, μg/L.

The mathematical framework presented above establishes the intelligent slow-release chemical tracer monitoring data interpretation model, which integrates dynamic release kinetics with real-time analytical processing capabilities. Based on monitoring data such as production and tracer concentration, this model can be used to achieve key parameters such as liquid production and water content in each monitoring layer.

3. Actual Well Application

The certain reservoir is located in the south-central Bohai Sea, comprising vertically successive strata from the Quaternary Pingyuan Formation, Neogene Minghuazhen Formation, Guantao Formation, Paleogene Dongying Formation, down to the Paleozoic. The Guantao Formation is the main oil-bearing stratum, and the reservoir contains multiple oil-bearing layers and exhibits strong heterogeneity [40,41]. It currently maintains high production and a low water cut, and some wells are starting to produce water. In order to understand the water production law and production profile of well A, intelligent slow-release tracer monitoring is carried out. The production section of well A is the Guantao Formation, with a large thickness and multiple oil production groups. The logging interpretation results of production sections are shown in

Table 1. Well logging interpretation results for production section of well A.

Horizons	Oil Group	Top Depth (m)	Bottom Depth (m)	Layer Thickness (m)	Stratum Resistivity (Ω·m)	Porosity (%)	Permeability (mD)	Hydrocarbon Saturation (%)	Explanation
N1gu	L60	1520.3	1538.7	18.4	8.1	25.77	1349	56.33	oil layer
	L62	1553.4	1556.7	3.3	6.05	22.3	579.5	49	oil layer
	L64	1559.6	1581.8	22.2	8.98	21.93	576.25	56.6	oil layer
	L70	1588.5	1599.9	0.75	5.55	20.5	236.5	47.2	oil layer
	L72	1604.3	1617.7	13.4	7.17	22.62	685.31	52.28	oil layer
	L74	1618.3	1628.3	10	7.23	21.68	499.35	52.03	oil layer
N1gl	L80	1640.9	1658.2	17.3	7.23	21.68	499.35	52.03	oil layer
	L82	1661.2	1669.9	8.7	7.23	21.68	499.35	52.03	oil layer
	L84	1677.8	1678.4	0.6	5.1	19.3	125	45.8	oil layer
	L86	1689.1	1699.1	10	6.52	20.89	374.57	49.95	oil layer
	L90	1718.7	1720.1	1.4	9.4	18.9	149	57	oil layer
	L92	1732.4	1745.4	13	7.01	19.70	216.19	50.92	oil layer
	L94	1752.3	1759.1	7.2	8.20	19.30	182.59	53.96	oil layer

.

In order to understand the zonal contribution analysis of oil–water production dynamics, the target production interval was divided into four discrete monitoring sections, and the screen short circuit was installed. Different intelligent slow-release tracers were arranged on the screen short circuits, respectively; each layer section was isolated by a separator. The specifications and tracer types of each section are shown in

Table 2. Short circuit specifications for each section of screen tube and tracer type.

Screen Pipe Short Joint Sequence	Outside Diameter (in)	Inner Diameter (in)	Length (m)	Top Depth (m)	Bottom Depth (m)	Oil Phase Tracer	Water Phase Tracer
1	6.002	4.892	6	1754.98	1760.97	OS-1	WS-1
2	6.002	4.892	6	1654.61	1660.61	OS-2	WS-2
3	6.002	4.892	6	1608.52	1614.51	OS-3	WS-3
4	6.002	4.892	6	1521.17	1527.17	OS-4	WS-4

. According to the depth of the screen short circuit combined with the depth of each oil group, the first section of the screen short circuit monitored the L94 oil group and below, the second section of the screen short circuit monitored the L92 oil group to the L82 oil group, the third section of the screen short circuit monitored the L80 oil group to the L72 oil group, and the fourth section of the screen short circuit monitored the L70 oil group to the L60 oil group. The oil and water production profile of the layers was monitored by the four screen short circuits.

The well was completed on 29 May 2023. The intelligent slow-release tracer monitoring string was installed. The sample analysis was divided into two stages. The first sampling period was from 1 June to 14 June, and 17 mixed samples were collected. The second sampling period was from 12 December to 16 December, and five mixed samples were collected. The water content of the mixed sample was between 20% and 99.6%. The laboratory separated 8 water samples (4#~17# mixed samples were all water-in-oil emulsions, and it was difficult for the laboratory to separate sufficient water samples for testing) and separated 21 oil samples (1# mixed sample had a high water content, and the laboratory did not separate sufficient oil samples for testing). In total, 8 water samples and 21 oil samples were actually sent for inspection. The changes in oil well production and water cut during the sampling of qualified samples are shown in Figure 2. As can be seen from Figure 2, the water cut dropped rapidly in the early stage of sampling, which may be caused by the completion fluid backflow. After fluid backflow was completed, the water cut was maintained at about 25%, and the liquid production and oil production of the well was basically stable. During the second stage of sampling, the oil well liquid production was about 30 m³/d, and the water content was maintained at about 20%.

Based on the oil and water samples separated in the laboratory, the concentrations of these tracers were detected. Considering the influence of fluid production on tracer release concentration, Equation (5) was used to adjust the test concentration by combining the dynamic production data of each layer. The corrected tracer concentration is shown in Figure 3 and Figure 4. Comparing these samples in the first and second periods, the concentration of WS-1 did not change much, and the concentration remained at about 160 μg/L. The concentration of WS-2 decreased significantly, from 170 μg/L to about 80 μg/L. The concentration of WS-3 increased significantly, from 70 μg/L to about 180 μg/L. The concentration of WS-4 decreased to a certain extent, from 120 μg/L to about 60 μg/L. Among the oil-soluble tracers, the output concentration of OS-1 was relatively high, between 100 and 210 μg/L, OS-2 and OS-3 decreased to a certain extent, and OS-4 decreased significantly. The changes in the tracer concentrations reflected the changing production characteristics of each layer.

According to the release characteristics of tracers and the production transience analysis of well A, the tracer production concentration was corrected, and the intelligent tracer interpretation method could be used to calculate the oil and water production of each section. Figure 5 showed the oil and water production of each section during the monitoring period. The well mainly produced oil, and the main oil-producing section was the first section. During the drilling and completion process, the working fluid invaded the formation, which caused an increase in water saturation and formation pressure. When the oil well was first put into operation, liquid production exceeded 60 m³/d. The oil production of the well significantly increased after the backflow of the invasion fluid. Due to the decrease in formation pressure in the later stage, oil well production decreased to 30 m³/d, and the oil and water production in each section remained relatively stable.

Figure 6 showed the sectional water yield profile during the monitoring period, and it could be found that the water yield of each test layer decreased significantly during the second sampling process. The first and third sections were the main water production contribution sections, the proportion of the first section decreased from 33.5% to 31.6%, and the water production decreased from 16.4 m³/d to 6.6 m³/d. The proportion of the third stage increased from 14.5% to 38.1%, and the water production decreased from 8.0 m³/d to 7.8 m³/d. The second and fourth sections were the low-water-production contribution sections; the proportion of the second section decreased from 27.6% to 17.7%, from 15.6 m³/d to 3.6 m³/d. The proportion of the fourth stage decreased from 24.4% to 12.7%, and the water production decreased from 11.3 m³/d to 2.6 m³/d.

Figure 7 showed the oil production profile of the monitoring well during the monitoring periods. By comparing the oil production between two sampling stages, it could be observed that the oil production in each segment had significantly decreased. The first and third stages were the main oil-producing segments. Specifically, the first stage’s contribution ratio increased from 37.7% to 51.8%, with production decreasing from 21.0 m³/d to 12.3 m³/d. The third stage’s contribution ratio decreased from 25.1% to 23.2%, with its production decreasing from 12.2 m³/d to 5.6 m³/d. The second and fourth stages were low oil-producing segments. The second stage’s contribution ratio decreased from 19.7% to 15.1%, with its oil production decreasing from 10.0 m³/d to 3.6 m³/d. The fourth stage’s contribution ratio decreased from 17.4% to 9.9%, with its oil production decreasing from 6.9 m³/d to 2.3 m³/d.

After the well was put into production, it produced 69.2 m³/d of liquid during the first sampling period, exhibiting a comprehensive water cut of 27.6%. After 194 days of production, it yielded 30 m³/d of liquid during the second sampling period, and the comprehensive water cut dropped to 20.7%. During the first sampling period, the liquid production of all monitored intervals was relatively high. In the second sampling period, the medium-liquid production profile was adopted, mainly in the first and third sections, with liquid production of 14.2 m³/d and 7.9 m³/d, respectively. The liquid production of the second and fourth sections decreased significantly, dropping to 4.71 m³/d and 3.1 m³/d respectively (Figure 8).

The water cut of each monitored interval could be calculated based on the water production and oil production of each interval (Figure 9). There was no high water cut between these intervals, and the water cut of each section synchronously decreased. The water cut of each section was not higher than 30%. From the perspective of sections, the water cut of the first section was relatively low, at 13.9%; the water cut of the second, third, and fourth sections was slightly higher, ranging from 23.2 to 29.8%, and there was no obvious water invasion feature.

In the development of oil and gas reservoirs, the monitoring of production profiles in the commingled completions is of great significance for evaluating the development effect and optimizing reservoir management. In the early stage, production logging is generally used to monitor the liquid production of each section. During the monitoring process, production logging requires lowering of the pipe string, which has high requirements for the operation well site and the wellbore structure, resulting in the inability to carry out monitoring in some wells. At the same time, production logging can only monitor the liquid production of each section but cannot obtain the water cut. For wells with a high water cut, it is impossible to effectively take water control and oil production measures. Intelligent slow-release tracer monitoring technology has solved the shortcoming of production logging being unable to achieve continuous monitoring. By inserting oil-phase intelligent tracers and water-phase intelligent tracers, it is possible to continuously monitor the oil and water production conditions of each monitoring section and determine the production and water cut of each monitoring section. Due to the fact that this technology solves the production profile issue by dissolving tracers in production fluid, it can be used for monitoring the production profile of low-permeability reservoirs and unconventional oil and gas reservoirs. For multi-layer commingled production wells, single-layer water invasion often occurs during the water injection production process. This technology can realize the accurate judgment of the water invasion layer and provides effective guidance for later water control and oil production measures.

4. Conclusions

With the development of the petroleum industry, intelligent slow-release chemical tracer monitoring technology has been widely used in oil fields. This paper studied the monitoring principle, interpretation method, and application of intelligent slow-release chemical tracer monitoring data. The following conclusions are drawn:

(1)

According to the release kinetics of intelligent tracers, multiphase flow characteristics in wellbore, and tracer dissolution dynamics, a production fluid profile calculation model and a water cut calculation model were established, which realize the quantitative calculation of the production profile and water cut in the multi-layer-combined production well. This provided a theoretical basis for the interpretation of intelligent slow-release tracer monitoring data.

(2)

Well A exhibited high production rates and a low water cut; the contribution of oil production varied greatly among different layers during the period of stable production. The first and third sections were identified as the main contributors, accounting for 51.8% and 23.2% of production, respectively, while the second and fourth sections showed lower contributions of 15.1% and 9.9%. The water cut of each section was below 30%.

(3)

The intelligent slow-release tracer monitoring technology realized the continuous monitoring of production profile status in well A, which contributed to understanding the production status and flooding characteristics of each layer. This provides effective guidance for the adjustment of the production system and the implementation of related measures.