Increasing Lifetime of Tubing Pump with Sand Problem in Field X Zona 1 Pt Pertamina EP

Field X Zona 1, operated by PT Pertamina EP, is a mature oil field currently undergoing secondary recovery. Despite ongoing recovery efforts, production decline remains inevitable at this stage. Among the artificial lift methods utilized in Field X, the Sucker Rod Pump (SRP), or tubing pump, is the most dominant, accounting for 50% of total implementation. In 2023, data from Field X recorded the highest sand-related challenges, leading to a significant production loss of 10,809.50 barrels of oil during well service jobs, translating to an estimated revenue shortfall of IDR 12.9 billion. A structured analytical approach was employed, integrating the Fishbone Diagram, Failure Mode and Effect Analysis (FMEA), and Pareto Diagram to diagnose the root causes of sand production. The findings highlight a critical gap in the equipment and method aspect. Both of them were identified as each contributing to 41.7% of sand-related issues, according to Pareto analysis. To address these challenges, multiple alternative solutions were explored, that are conducting sucker rod bump-down jobs using cranes, installing premium sand screens in front of perforations, implementing FractPack stimulation techniques, and modifying tubing pump accessories for enhanced sand control. The Analytical Hierarchy Process (AHP) was employed to assess these alternatives based on three critical factors, that are risk, cost, and job delivery time. The consistency ratio (CR) for each criterion was calculated, with CR of overall criteria at 0.02052, CR of risk sub-criteria at 0.0960, CR of cost sub-criteria at 0.0934, and CR of job delivery time sub-criteria at 0.0780. Since all CR values are below the threshold, the decision-making process is deemed consistent and reliable. Among the proposed solutions, modifying tubing pump accessories emerged as the most effective, securing the highest final score of 0.5157. The design requirements for this modification include multi-stage sand filtration, ease of fabrication, and simplified installation. The effectiveness of this solution was further validated through pilot tests in wells WS-453 and WS-414, where operators collected fluid samples and conducted BS&W (Basic Sediment & Water) analysis. Remarkably, no sand was detected in the produced fluids at the surface, confirming the success of this approach in mitigating sand-related production losses in mature fields.

Introduction

Field X Zona 1, operated by PT Pertamina EP, is a mature field currently in the secondary recovery phase. This phase presents challenges, as oil production is generally lower than in the primary phase (Ahmed, 2010), requiring a balance between cost efficiency and the adoption of advanced technologies (Brown, 1980). The distribution of artificial lift methods employed in Field X, highlighting the predominance of the Sucker Rod Pump (SRP) or tubing pump, which constitutes 50% of total usage.

To counteract this decline and maintain oil production at its potential, well services play an essential role. The distribution of artificial lift methods employed in Field X, highlighting the predominance of the Sucker Rod Pump (SRP) or tubing pump, which constitutes 50% of total usage. In the Table I highlights the growing impact of sand-related problems on revenue in oil well operations from 2020 to 2023. In 2023, according to KUPS data from Field X, sand problems reached their highest level, resulting in a loss of 10,809.50 barrels of oil during well service jobs, equivalent to a revenue shortfall of IDR 12.9 billion.

Table I. Details of Recorded Revenue Losses from Sand Problem

Sand problem in year	Job days	Lost production during WS job (Bbls)	Lost revenue during job
2023	419.88	10809.50	Rp 12,971,400,000
2022	207.40	4936.11	Rp 5,923,332,000
2021	302.44	7899.44	Rp 9,479,325,000
2020	222.92	5491.05	Rp 6,589,260,000

In this research, the authors have three objectives. There were identifying the primary cause of sand problems that result in LPO and contributes a loss of revenue up to IDR 12.9 billion, developing strategies that can extend the lifespan of oil well production affected by sand problems in the mature field, and identifying the best strategy or solution in addressing the sand issues.

Literature Review

Fishbone Diagram

Fishbone diagrams, also known as cause-and-effects diagram or Ishikawa, are valuable for displaying hypothesized relationships between potential causes and the problem under investigation. The fishbone diagram is often combined with the “five-whys” technique to effectively determine the root cause of a problem (Augustine Kumah, 2024). Once the fishbone diagram is constructed, the analysis progresses by identifying which of these potential causes are genuinely contributing to the problem.

Failure Mode and Effects Analysis (FMEA)

Failure Mode and Effects Analysis (FMEA) has been used for many decades and has a longstanding history as a method for supporting product design, manufacturing processes, service, and maintenance (Kiran, 2017). FMEA is a method used to identify and understand potential failure modes and the effects of damage in processes or equipment. By prioritizing risks and implementing corrective actions, it helps prevent failures, ultimately enhancing reliability, quality, and safety. This proactive approach allows companies to anticipate and prevent issues, achieving high reliability in products and processes within shorter development times and within budget (Carlson, 2012). The evaluation involves assessing three key factors where severity (S) measures the impact of a failure on the system, occurrence (O) represents the likelihood of the failure happening, and detection (D) indicates the probability of identifying the failure (Tata Kerja Individu, 2018). Each factor is assigned an integer value from 1 to 10 by experts, and the overall risk for a failure mode is quantified using the Risk Priority Number (RPN), which is calculated as the product of these three factors (Joanna Fabis-Domagala, 2021).

Analytical Hierarchy Process (AHP)

MCDM methods provide the distinct benefit of integrating various criteria or attributes to achieve a well-rounded decision-making result (Saeed Al-Ali, 2024). The Analytic Hierarchy Process (AHP) is a widely used MCDM method, introduced by Saaty (1980), is a fundamental tool for multi-criteria decision-making. It offers a structured method for examining complex decision problems, aiming to identify the best alternative based on a specific set of criteria (Al-Harbi, 1999). AHP provides a detailed, step-by-step framework to guide users through the decision-making process.

The AHP methodology relies on two main assumptions: that alternatives and criteria can be ranked linearly by importance, and that matrices from pairwise comparisons will consistently reflect this ranking. However, in real-world decision-making, human judgments are rarely fully consistent, especially in complex, multi-criteria situations (Munier, 2021).

Methodology

The methodology of this research begins with identifying the business issue (Sachdeva, 2023), followed by root cause analysis using analytical methods such as Fishbone, FMEA, and Pareto Diagram. Based on these findings, alternative business solutions are generated using relevant data and insights from historical job records, drawing on proven methods and strategies successfully implemented in similar situations. A selected solution is determined using quantitative analysis, such as Likert questionnaires, followed by calculations using the AHP method. The selected solution is then implemented in a pilot well before being scaled up for full-field implementation in Field X Zona 1.

Data Collection

In this research, data collection was divided into two sections. Those are data collection for root cause analysis and data collection for determined solutions.

Data Collection for Root Cause Analysis

To analyze the root cause, the author utilizes both primary and secondary data. Primary data is gathered through interviews, which serve as a qualitative research method. Semi-structured interviews are utilized for data collection, allowing the researcher to prompt or encourage interviewees to provide additional information or elaborate on intriguing points.

These interviews involve one-on-one discussions to gain in-depth insights into participants’ perspectives (Carolyn Boyce, 2006) on the challenges and potential solutions related to sand control. As part of the data collection process, the author organized focused interview sessions with the company’s stakeholders.

Data Collection for Selection of Solutions

Subsequent to the identification of root causes, the study will formulate alternative solutions. To ensure a structured and quantifiable evaluation, the relative importance of selection criteria will be assessed using a Likert-scale questionnaire distributed to key stakeholders. The Likert scale is a widely used tool in research for capturing varying degrees of agreement, importance, or satisfaction. It allows respondents to express their opinions on a continuum, ensuring measurable and analyzable data (Ankur Joshi, 2015).

Data Analysis Method

In this research, the author uses qualitative and quantitative analysis. From the results of interviews, the qualitative data collected is analyzed to identify patterns and correlations among responses. These connections are then grouped and mapped onto a fishbone diagram, visually representing the factors leading to the root cause of the problem. Building on this, the analysis proceeds with FMEA to further evaluate and understand the potential failure modes for each case, ensuring a systematic approach to addressing sand-related challenges (Robert Jacobset al., 2018).

Quantitative methods focus on collecting and analyzing numerical data to quantify relationships between variables, enabling a structured and measurable evaluation. In this research, the results from the FMEA are used to develop a Pareto diagram, which identifies and prioritizes the most critical root causes that need to be addressed. Based on these prioritized causes, alternative solutions are generated. To ensure a robust decision-making process, the study then employs qualitative analysis using the AHP to compare and evaluate the alternatives systematically, ensuring the most effective solution is selected.

In the decision-making process, it is essential to first define the criteria that influence the selection of the most suitable solution. In this research, the author identifies three key criteria, which are explained as Table II.

Table II. Criteria for Decision Making

Criteria	Description	Importance
Risk	Potential hazards, such as blow-outs, fires, or well loss, must be identified.	Mitigating risks is critical to avoid failures, enhance operational safety, and ensure long-term success.
Cost	The total cost of implementation, encompassing material, labor, and operational expenses, should be minimized, subject to quality and well economics considerations.	Managing costs is essential to ensure financial feasibility and resource optimization for the business.
Job’s delivery time	The time required to complete and deliver the solution, from initiation to full implementation. Faster delivery is prioritized to minimize downtime and production losses.	Shorter delivery times are crucial to reducing operational disruptions and restoring productivity quickly.

Based on the criteria mentioned above, a Likert-scale questionnaire is deployed to identify and rank the proposed solutions.

Before obtaining the final results, the consistency of the pairwise comparisons is verified by checking the Consistency Ratio (CR). If the CR value is ≤0.1, it indicates that the PCM is consistent, and the results can be considered reliable. However, if the CR value exceeds 0.1, the authors will review the questionnaire responses, validate the respondents’ answers, and revise the PCM as necessary. Once the CR meets the acceptable threshold, the rank of each solution is finalized. These rankings provide a systematic basis for selecting the most effective solution to address the sand handling issue.

Result and Discussion

Analysis

Root Cause Analysis

The findings from the interviews are then organized and mapped into categories using a Fishbone diagram, as shown in Fig. 1. The diagram highlights the key factors contributing to sand-related issues in oil production, leading to significant operational challenges and a revenue loss of IDR 12.9 billion due to the Loss of Production Opportunity (LPO). These factors include a lack of training on sand control, limited knowledge of sand treatment technologies, and incomplete comprehensive production sample analysis by engineers, which reflect human-related gaps. Methodological shortcomings, such as inadequate sand handling processes and ineffective sand screens. Material-related issues include the limited mesh size (50 mesh) of sand screens and the presence of oversized sand particles.

Fig. 1. Fishbone diagram of sand problem.

Environmental factors, such as low reservoir pressure, weakened sand bonds, and reduced rock compaction, contribute to excessive sand release from formations. Additionally, equipment issues, including pump failures, ineffective sand control accessories, and the absence of dual-process sand handling equipment.

Combining primary and secondary data, the author makes correlation analysis into FMEA as shown in Table III.

Table III. Fmea Analysis and Pareto Diagram

No	Process description	Failure mode	S	O	D	RPN
A	No equipment for handling sand with two processes simultaneously	Sand may enter the pump, potentially leading to pump failure	8	9	6	432
B	Sand handling method still uses conventional sand screens/desanders	Sand may enter the pump, potentially leading to pump failure	8	9	6	432
C	50-mesh sand screen size	Sand can still pass through the sandscreen and there is no settling time	4	8	4	128
D	Lack of training on sand control	Improper choice of pump accessories or treatment for sand production wells	3	3	3	27
E	Low reservoir pressure	High amount of sand produced with the fluid	2	4	2	16

The highest RPN values for both issues are among the highest, contributing significantly to the overall problem at 41.7%, as illustrated in the Pareto Diagram, Fig. 2 The analysis moving forward will prioritize resolving these equipment and method related challenges to develop robust and effective alternative solutions.

Fig. 2. Pareto diagram of root causes.

Generate Alternative Business Solutions

The primary issues stem from equipment and methods, necessitating the development of alternative business solutions. These solutions are formulated based on relevant data and insights gathered from historical job records. The proposed solutions are outlined as follows:

Sucker Rod Bump Down Job With Crane

A sucker rod bump down job is a maintenance operation conducted to adjust or replace sucker rods in a rod pump system, typically used in oil wells to lift fluid to the surface. This procedure is often necessary to correct spacing issues, replace damaged rods, or release the stuck plunger in the pump. The challenge is selecting the appropriate crane capacity (Hana, 2018) to safely pull the sucker rod.

Premium Screen Installation in Front of the Perforation

The installation of a premium screen in front of the perforation zone is a common sand control technique in oil and gas wells. This method helps to prevent sand from entering the wellbore while allowing hydrocarbons to flow freely (Penberthyet al., 1992). By using this option, the challenge is choosing the optimum gap since reducing the gap size minimizes sand production but leads to greater flow restrictions, while enlarging the gap size facilitates fluid flow but increases the risk of sand production (Woiceshynet al., 2010).

FractPack Stimulation

The frac-pack technique using proppant addresses deep formation damage by creating fractures of 50 to 100 feet in length while also providing sand control (Andretiana Putranti, 2019). Resin, either precoated on the proppant or applied during pumping, is activated by the bottomhole temperature. This activation causes the resin to bond the proppant grains to each other and to the formation, forming a strong structure. The resin’s role is to secure the proppant in place and reduce or prevent sand production from the formation. The challenges of frac-pack operations include optimizing fracture geometry, managing sand production and proppant flowback, and controlling fluid leak off.

Modifying the Accessories of Tubing Pump

Modifying the accessories installed on tubing pumps is a proposed alternative solution to address the sand problem (Quinteroet al., 2017). The DR&O for this solution includes implementing multi-stage sand filtration, ensuring ease of fabrication, and simplifying the installation process. However, a key challenge lies in ensuring quality control, particularly in welding areas, as any oversight could result in the accessory detaching and becoming a “fish” in the wellbore.

Determine Selected Solution

The AHP method is employed to assess and select the most appropriate alternative to address the sand problem.

Defining the Decision Hierarchy

The decision hierarchy begins by pinpointing the core issue, which is the sand problem. This structured approach ensures a clear pathway from problem identification to actionable solutions. The visual representation of the decision hierarchy is illustrated in Fig. 3.

Fig. 3. The decision hierarchy.

Making Pairwise Comparison

Based on the summary of Likert-Questioner’s result, the criteria matrix is shown in Table IV, the sub-criteria risk matrix in Table V, the sub-criteria cost matrix in Table VI, and the sub-criteria job delivery time matrix in Table VII.

Table IV. Pairwise Comparison Matrix on Criteria

Criteria	Risk	Cost	Job’s delivery time
Risk	1.0000	5.0000	9.0000
Cost	0.2000	1.0000	3.0000
Job’s delivery time	0.1111	0.3333	1.0000
TOTAL	1.3111	6.3333	13.0000

Table V. Pairwise Comparison Matrix on Sub-Criteria of Risk

Risk	Bump-down	Premium screen installation	Frac. Stim.	Acc. Modf.
Bumpdown	1.0000	0.1429	0.2000	0.1111
Premium screen installation	7.0000	1.0000	5.0000	0.5000
FracPack stimulation	5.0000	0.2000	1.0000	0.1429
Accessories modification	9.0000	2.0000	7.0000	1.0000
TOTAL	22.0000	3.3429	13.2000	1.7540

Table VI. Pairwise Comparison Matrix on Sub-Criteria of Cost

Cost	Bump-down	Premium screen installation	Frac. Stim.	Acc. Modf.
Bumpdown	1.0000	7.0000	9.0000	0.3333
Premium screen installation	0.1429	1.0000	3.0000	0.1429
FracPack stimulation	0.1111	0.3333	1.0000	0.1111
Accessories modification	3.0000	7.0000	9.0000	1.0000
TOTAL	4.2540	15.3333	22.0000	1.5873

Table VII. Pairwise Comparison Matrix on Sub-Criteria of Job’s Delivery Time

Job’s delivery time	Bump-down	Premium screen installation	Frac. Stim.	Acc. Modf.
Bumpdown	1.0000	7.0000	9.0000	3.0000
Premium screen installation	0.1429	1.0000	3.0000	0.1667
FracPack stimulation	0.1111	0.3333	1.0000	0.1250
Accessories modification	0.3333	6.0000	8.0000	1.0000
TOTAL	1.5873	14.3333	21.0000	4.2917

Determining the Relative Weight

To calculate the relative weight, the pairwise comparison matrix must first be converted into a normalized format. Fig. 4 illustrates the process of normalizing the criteria matrix and determining the relative priority (eigenvector) of each criterion. The normalization process involves dividing each value in the matrix by the total of its respective column, ensuring that the sum of each column equals one. Once normalized, the relative priority of each criterion is obtained by averaging the row values, resulting in the eigenvector, which represents the weight of each criterion in the decision-making process. As shown in the figure, the highest priority is assigned to “Risk” with a weight of 0.7482, followed by “Cost” at 0.1804, and “Job’s Delivery Time” at 0.0714.

Fig. 4. Determining eigenvector or relative priority of criteria.

The eigenvector results for the risk sub-criteria, “Accessories Modification” holds the highest priority, indicating the lowest risk, with a weight of 0.5270. In practice, accessories modification is performed in the workshop and installed before the tubing pump enters the well, minimizing operational hazards. Conversely, “Bumpdown” has the highest operational risk, with a weight of 0.0417. This indicates that the bumpdown job, which involves using a crane, carries significant risks due to the unknown load of a stuck sucker rod caused by sand accumulation on the tubing pump plunger. This situation has the potential to cause overload, leading to property damage and serious operator injury.

The eigenvector results for the cost sub-criteria, “Accessories Modification” holds the highest priority, indicating the lowest cost, with a weight of 0.5502. This is because the accessories are developed and fabricated in-house using pumpshop materials, generally keeping costs below IDR 10 million. The other side, “FracPack Stimulation” has the highest operational cost, with a weight of 0.0408. This high cost is attributed to the need for service contracts with contractors to perform the job, as well as the relatively expensive materials, which are calculated based on the volume of proppant pumped into the wellbore until the fracturing job is completed. In general, a FracPack Stimulation job costs approximately IDR 2.4 billion.

The eigenvector results for the job’s delivery time sub-criteria. “Bumpdown” holds the highest priority, indicating the fastest job’s delivery time, with a weight of 0.5615. This is because a bumpdown job with a crane can be completed within a single day. On the other hand, “FracPack Stimulation” has the longest job’s delivery time, with a weight of 0.0425. This extended duration is due to the reliance on a service contract. If a contract is available, mobilizing the unit and equipment from the contractor’s yard to the well takes 14 days. However, if no contract is in place, the tendering process alone requires approximately six months before the job can commence.

Checking the Consistency of Judgment

The reliability of the final decision heavily depends on how consistently the decision-maker evaluates and compares options throughout the pairwise comparison process. For each item, multiply every value in the first column of the pairwise comparison matrix by the relative priority of the first considered item. Repeat this process for all other items. Then, sum the values in each row to generate a vector known as the “weighted sum”. Each element in this vector is then divided by its corresponding priority value, and the average of these resulting values is calculated, ${\lambda }_{max}$. Finally, the consistency index (CI) with $n$ is the number of items being compared and consistency ratio (CR) are computed. If the CR values above 0.10, it is suggesting inconsistent judgments (Saaty & Vargas, 2012).

(1) $$\begin{array}{r}CI=\frac{{\lambda }_{max}-n}{n-1}\end{array}$$

(2) $$\begin{array}{r}CR=\frac{CI}{RI}\end{array}$$

Table VIII is the commonly used RI values for matrices of different sizes.

Table VIII. Random Index Values are Used in the AHP

n	1	2	3	4	5	6	7
RI	0.00	0.00	0.58	0.90	1.12	1.24	1.32

Fig. 5 shows the calculation sheet of the criteria’s matrix. The value of ${\lambda }_{max}$ is 3.0293, and the CI is 0.0146. Since the matrix has three elements, the random index (RI) is 0.58. The CR is calculated to be 0.02052, which is less than the threshold of 0.10. This indicates that the judgments of the criteria from the survey are consistent.

Fig. 5. Calculating ${\lambda }_{max}$ to determine the consistency of criteria.

The author then proceeds to evaluate the consistency of judgment for alternative solutions based on each sub-criterion. The ${\lambda }_{max}$ value for the risk sub-criteria is 4.2592. Using this value, the CI is calculated as 0.0864. Since the risk sub-criteria matrix contains four elements, the RI is 0.90, resulting in a CR of 0.0960. This CR value indicates that the survey used to select alternative solutions based on risk sub-criteria is consistent.

Moving on to the next sub-criterion, cost. The ${\lambda }_{max}$ value for the cost sub-criteria is 4.2522. Using this value, the CI is calculated as 0.0841. Similar to the risk sub-criteria matrix, the cost sub-criteria matrix also contains four elements, resulting in a RI of 0.90 and a CR of 0.0934. This CR value confirms that the survey used to determine alternative solutions based on cost sub-criteria is consistent.

Continuing from the cost sub-criteria, the next sub-criterion analyzed is job’s delivery time. The ${\lambda }_{max}$ value for this sub-criterion is 4.2105. Using this value, the CI is calculated as 0.0702. Similar to the previous sub-criteria matrices, the job’s delivery time sub-criteria matrix also consists of four elements, resulting in a RI of 0.90 and a CR of 0.0780. This CR value confirms that the survey used to determine alternative solutions based on job’s delivery time sub-criteria is consistent.

The consistency analysis of the criteria and sub-criteria matrices confirms the reliability of the survey judgments in selecting alternative solutions.

Developing a Priority Ranking

The total priority for each alternative solution is calculated by adding up the products of the criteria’s weight (relative to the overall goal) and the alternative solution’s preference score for those criteria. By ordering these total priority values, the author obtain the AHP ranking of the decision options. As shown in Fig. 6, “Accessories Modification” achieves the highest final score of 0.5157, making it the top-ranked solution.

Fig. 6. Priority ranking of alternative solutions.

This is followed by “Premium Screen Installation” with 0.2604, “Bumpdown” with 0.1304, and “FracPack Stimulation” with the lowest score of 0.0935. These rankings highlight that “Accessories Modification” is the most preferred alternative, while “FracPack Stimulation” ranks the lowest in terms of overall priority.

Business Solution

From the previous sub-chapter, the best alternative solution is modifying the accessories of tubing pump. The design requirements are multi-stage sand filtration, ensuring ease of fabrication, and simplifying the installation process. During the initial phase, a tubing shroud or sand barrier assists in the preliminary separation of sand particles. As fluid flows through the system, the sand collides with the outer surface of the tubing, leading to its accumulation in the wellbore. The illustration of principle the usage of sand barrier as Fig. 7.

Fig. 7. Illustration of sand separation through sand barrier.

The second stage involves using a spiral plate welded to the end of the intake or suction pipe, similar to the intake pipe on a desander. The concept is to create a cyclone effect in the fluid flowing through the profile. When the fluid is in a cyclone profile, the sand particles experience centrifugal force and separate from the fluid, dropping into a mud anchor (Suryantoroet al., 2023). The author proposes an improvement to the original design where the spiral plate had a length of 120 mm and a 45° angle between the spiral and the pipe axis. Fluid flow simulation reveals that this design does not produce an optimal cyclone profile, as shown in Fig. 8.

Fig. 8. The Fluid’s cyclone profile is not optimal in the current design.

The author improves the calculation by utilizing the Reynolds Number equation. To ensure turbulent flow, a Reynolds Number ($N_{Re}$) of 4,000 is used (Munsonet al., 2009). The equation incorporates key parameters, including spiral length ($L$), fluid viscosity ($\mu$), fluid density ($\rho$), and fluid velocity ($v$). The formula is as follows:

(3) $$\begin{array}{r}{N}_{Re}=\frac{L.\mu }{\rho .v};\text{or}\end{array}$$

(4) $$\begin{array}{r}L=\frac{N_{Re}.\mu }{\rho .v}\end{array}$$

Based on Field X’s field properties, the spiral length was determined to be 79 cm and rounded up to 80 cm. The spiral’s angle relative to its axis was also calculated, ranging between 45° and 60°, ensuring that sand does not settle on the plate (Tchobanoglouset al., 2003). A follow-up simulation was conducted, yielding results of Fig. 9.

Fig. 9. Flow simulation result of new spiral profile.

In Fig. 9, at point “A”, the velocity profile varies between angles of 45° and 60°. The optimal profile for forming the cyclone is achieved with a 60° spiral relative to its axis. At point “B,” located at the entrance of the suction pipe, the velocity profile at 60° is classified as fully developed. In a fully developed flow, the velocity profile remains consistent across any cross-section of the pipe (Munsonet al., 2009). To transition turbulent flow back to laminar within the suction pipe, achieving a fully developed profile is essential. This is crucial because maintaining laminar flow inside the suction pipe helps reduce pressure drop before reaching the standing valve.

The integration of the first and second stages of sand separation before the fluid enters the tubing pump involves the use of additional components such as a perforated plate, perforated tubing, and a mud anchor, as detailed in Fig. 10.

Fig. 10. Technical drawing of tubing pump’s modification accessories.

This design not only enhances sand separation efficiency but also ensures ease of fabrication, following the checklist provided in Fig. 11, and simplifies the installation process.

Fig. 11. Dimension checklist of tubing pump’s modification accessories.

Before implementing additional accessories across all wells experiencing sand-related issues with tubing pumps as their artificial lift method, the author first fabricated and installed the accessories during well service activities at the end of January 2025. Two wells, WS-453 and WS-414, were selected for validation. Based on production results, well operators collected fluid samples and conducted BS&W analysis in the lab to determine the amount of sand carried to the surface. The results, as shown in Fig. 12, demonstrate that the accessories effectively separate sand before the fluid enters the tubing pump, significantly reducing sand production.

Fig. 12. BS&W analysis of well’s sample fluid before and after installing additional accessories on the tubing pump.

Conclusion and Recommendation

Conclusion

The sand problem in oil wells has led to Loss of Production Opportunity (LPO), resulting in substantial revenue losses of up to IDR 12.9 billion. This is primarily due to the absence of equipment capable of handling sand using two simultaneous processes. Additionally, the existing sand handling methods rely on conventional sand screens and desanders, which each contribute to 41.7% of issues based on the Pareto diagram analysis.

To mitigate the impact of sand-related challenges and enhance oil well production longevity, several strategies can be implemented. These include conducting sucker rod bump-down jobs with a crane, installing premium screens in front of perforations, conducting FractPack stimulation, and modifying tubing pump accessories. These solutions directly address the root causes of sand issues, ensuring better sand control and reducing production disruptions.

The Analytical Hierarchy Process (AHP) was utilized to determine the most effective alternative solutions based on three key criteria, that are risk, cost, and job delivery time. Among the evaluated options, modifying the tubing pump accessories emerged as the top-ranked solution, achieving the highest final score of 0.5157. Based on production results from pilot wells WS-453 and WS-414, where well operators collected fluid samples and conducted BS&W analysis, sand was not found in the produced fluid at the surface. This indicates that the solution is effective in addressing the sand issue in the mature field.

Recommendation

Establish Standard Operating Procedures (SOPs) for Fabrication

It is crucial to develop and implement a company-wide operational standard procedure (SOP) for manufacturing modified accessories based on this research. This SOP will serve as a guideline to ensure technicians follow precise fabrication steps, minimizing errors and maintaining consistency in production

Enforce Non-Destructive Testing (NDT) for Quality Assurance

To prevent weld failures and connection breakdowns, every fabricated accessory must undergo a Non-Destructive Test (NDT) before installation. This quality control measure will ensure structural integrity, reduce the risk of detachment, and prevent equipment failure inside the wellbore.

Collect and Analyze Sand Production Rate Data for Each Well

To ensure effective sand separation, it is essential to collect and analyze sand production rate data for each well. This data will help determine the optimal mud anchor length, preventing potential operational issues. If the mud anchor is too long, there is a high risk of sand accumulation at the wellbore’s end, which could cause the accessory to become stuck and cannot pull-out when well service activity. If the mud anchor is too short, it may fail to hold enough sand, leading to blockage at the intake pipe, ultimately hindering production flow.