bluuebunny/crossref_metadata_2025 huggingface dataset:
The Development and Field Applications of Ultra-Deepwater Structural Conductor Jetting in Western South China Sea
Authors: Li Zhong
Published: October 2021

Lingshui X-1 block is located in ultra-deepwater region in western South China Sea. Drilling in this area are encountering many technical problems, such as low temperature, poor lithology in shallow formation, low fracture pressure gradient, gas hydrate and shallow geological hazards, which bring great technical challenges to subsea wellhead stability (Yang et al., 2013). In order to ensure wellhead stability and improve top-hole operation efficiency, jetting technology was used for spud-in. First of all, carrying capacity curve of structural conductor was obtained from mechanics analysis of shallow seabed soil in Lingshui X-1 block. Secondly, structural conductor size selection and load analysis were carried out to determine safe setting depth of structural conductor in Lingshui X-1 block. Finally, bit stick-out, bit size selection, Weight on Bit (WOB) and pump rate were optimized on the basis of comprehensive analysis of ultra-deepwater under top-hole jetting technology and BHA characteristics. Well LSX-1-1 was taken as an example to illustrate field operation for top-hole jetting. This successful case of top-hole jetting technology in Lingshui X-1 block of western South China Sea could provide technical guidance for future drilling activity in similar ultra-deepwater wells.
Unravel Carbonate Reservoir Heterogeneities Through 3D Seismic Neural Network Application: A Machine Learning Based Approach and Workflow
Authors: Ramadhan Dimmas , Laya Krishna Pratama , Rizkiaputra Ricko , Sinlae Esterlinda , Subekti Ari , Iswara Anindyajati Anak Agung Gde
Published: October 2021

The availability of 3D seismic data undoubtedly plays an important role in reservoir characterization. Currently seismic technology continues to advance at a rapid pace not only in the acquisition but also in processing and interpretation domain. The advance on this is well supported by the digitalization era which urges everything to run reliably fast, effective and efficient. Thanks to continuous development of IT peripherals we now have luxury to process and handle big data through the application of machine learning. Some debates on the effectiveness and threats that this process may automating certain task and later will decrease human workforce are still going on in many forums but still like it or not this machine learning is already embraced in almost every aspect of our life including in oil & gas industry.

Carbonate reservoir on the other hand has been long known for its uniqueness compared to siliciclastic reservoir. The term heterogeneous properties are quite common for carbonate due to its complex multi-story depositional and diagenetic facies. In this paper, we bring up our case where we try to unravel carbonate heterogeneity from a massive tight gas reservoir through our machine learning application using the workflow of supervised and unsupervised neural network. In this study, we incorporate 3D PSTM seismic data and its stratigraphic interpretation coupled with the core study result, BHI (borehole image) log interpretation, and our regional understanding of the area to develop a meaningful carbonate facies model through seismic neural network exercises. As the result, we successfully derive geological consistent carbonate facies classification and distribution honoring all the supporting data above though the limitation of well penetration in the area. This result then proved to be beneficial to build integrated 3D geomodel which later can explain the issue on different gas compositions happens in the area. The result on unsupervised neural network also able to serves as a quick look for further sweetspot analysis to support full-field development.
Successful Appraisal Cum Development of Shallow and Poorer Quality Gas Reservoir in Mature Field at Minimal Cost
Authors: Abdulhadi Muhammad , Ling Evelyn , Zubbir Ahmad Uzair , Said Hani Mohd , Elias Rohani , Sanchez Paul , Nor Nazri , Sharif Rizam , Ismail Nurhanim , Kew Yoong Hau , Ghapor Zamri Abdul , Ismail Abdul Malek
Published: October 2021

The Cement Packer approach has been successfully implemented in ExxonMobil Exploration & Production Malaysia Inc. (EMEPMI) to further develop minor gas reservoirs. The reservoir of interest is of relatively poor quality and has not been tested, thus making conventional development potentially not cost effective. Several viable approaches were identified and assessed to appraise and develop the reservoir. The cement packer method, which requires relatively minimal investment was then selected as being the most suitable in pursuing these behind casing opportunities.

Group 1 sands in Field A are the shallowest hydrocarbon reservoirs which are relatively thin and have low porosity and permeability. The existing completions are currently producing from deeper reservoirs, with the top packer located below the Group 1 sands. Developing the opportunities behind casing in these sands using the conventional pull tubing workover approach may be cost prohibitive. The cement packer approach, where the tubing was punched to create tubing-casing communication and cement was subsequently pumped through the tubing and into the casing, was identified as one of the potential cost effective solution. The hardened cement then acts as a barrier to satisfy operating guidelines. The reservoir was then additionally perforated, flow tested and successfully monetized.

Prior to well entry, tubing and casing integrity tests were performed to confirm the integrity. This step is critical to ensure that cement will only flow into the casing where the tubing was punched. Once the cement is hardened, pressure test from the tubing and from the casing indicated the cement has effectively isolated both tubulars. Subsequent Cement Bond Log and Ultrasonic Imaging Tool also displayed nearly 120m of fair to good cement above the target perforation depth. These data served as basis and proof that the cement packer was solid and the reservoir was ready for additional perforation. Taking into account the relatively poor reservoir quality, it was decided to perforate the reservoir twice with the biggest gun available to increase the probability of maximum reservoir contact while minimizing skin. Post perforation, a sharp increase in the tubing pressure was observed, indicating pressure influx from the reservoir. The casing pressure however, remained low, confirming no tubing-casing communication and thus the success of the cement packer. The well was later able to unload naturally from the high reservoir pressure. The work program also managed to confirm the producibility of the reservoirs.

This successful approach has opened up potential application to similar stranded reservoirs behind casing.
Improves Sucker Rod and Tubing Lifetime Applying the Wear Predict 99 Equation
Authors: Hidayat Teguh Rachman , Kurniawan Fajar , Nugroho Jalu Waskito Aji , Wibowo Aris Tristianto , Amal Panji Ikhlasul , Riskadarto Sebastianus
Published: October 2021

Finding new oil and gas that can be developed economically is getting more difficult and challenging today. To meet the oil and gas demand, it is therefore important to focus on the existing and already developed assets by applying new and more efficient technology and optimizing the use of existing equipment to increase production performance of the asset thus better recovery.

Sangasanga Field as mature oil field of Pertamina EP is producing its oil by the assistance of artificial lift. The artificial lifts applied in Sangasanga field are Sucker Rod Pump (SRP), Electrical Submersible Pump (ESP) and Hydraulic Pumping Unit (HPU) where SRP dominates with 84 units installed while ESP and HPU are 25 units and 15 units respectively.

According to the data of well service work history from 2018 to 2020, the failure of SRP and HPU was quite high. The main problem observed were the occurrence of leaking tubing and broken sucker rods. The study gathered the occurrence of failure and a method so called "WEAR PREDICT 99" was created to estimate SRP's buckling point and lifetime.

WEAR PREDICT 99 is a correlation derived from comparing neutral point calculated from formula with actual leak data of broken pipe or suction rod. The correlation then used for predicting the buckling point that represents the probable location of the leaking pipe or damaged suction rod.

This correlation allows to predict when and where the sucker rod will leak or break, therefore preventive measures to increase the lifetime of the SRP and HPU wells can be taken.
Malaysia's First Successful Implementation of Sand Consolidation Technology as a Primary Sand Control Method Executed during the Drilling Phase: Success Story and Lessons Learned
Authors: Kamaruddin Nadiah , A Karim Nurfuzaini , Hasmin M Ariff Naufal , Bela Sunanda Magna , Riyanto Latief , Samuel Orient Balbir , M Jeffry Suzanna Juyanty , Hashim Saharul , Balasandran Anandhadhasan , Ramirez Cristian , Mohan Ragupathy
Published: October 2021

Field A is a mature hydrocarbon-producing field located in eastern Malaysia that began producing in 1968. Comprised of multistacked reservoirs at heights ranging from 4,000 to 8,000 ft, they are predominantly unconsolidated, requiring sand exclusion from the start. Most wells in this field were completed using internal gravel packing (IGP) of the main reservoir, and particularly in shallower reservoirs. With these shallower reservoirs continuously targeted as good potential candidates, identifying a sustainable sand control solution is essential. Conventional sand control methods, namely IGP, are normally a primary choice for completion; however, this method can be costly, which requires justification during challenging economic times.

To combat these challenges, a sand consolidation system using resin was selected as a primary completion method, opposed to a conventional IGP system. Chemical sand consolidation treatments provide in situ sand influx control by treating the incompetent formation around the wellbore itself. The initial plan was to perform sand consolidation followed by a screenless fracturing treatment; however, upon drilling the targeted zone and observing its proximity to a water zone, fracturing was stopped. With three of eight zones in this well requiring sand control, a pinpoint solution was delivered in stages by means of a pump through with a packer system [retrievable test treat squeeze (RTTS)] at the highest possible accuracy, thus ensuring treatment placement efficiency. The zones were also distanced from one another, requiring zonal isolation (i.e., mechanical isolation, such as bridge plugs, was not an option) as treatments were deployed. While there was a major challenge in terms of mobilization planning to complete this well during the peak of a movement control order (MCO) in Malaysia, optimal operations lead to a long-term sand control solution. Well unloading and test results upon well completion provided excellent results, highlighting good production rates with zero sand production.

The groundwork processes of candidate identification down to the execution of sand consolidation and temporary isolation between zones are discussed. Technology is compared in terms of resin fluid system types. Laboratory testing on the core samples illustrates how the chemical consolidation process physically manifests. This is used to substantiate the field designs, execution plan, initial results, follow-up, lessons learned, and best practices used to maximize the life of a sand-free producer well. This success story illustrates potential opportunity in using sand consolidation as a primary method in the future.
A Deep Learning Model to Intelligently Identify the Working Status of Screw Pumps for Oil Well Lifting
Authors: Wang Zhen , Dong Yeliang , Zheng Xin , Wang Xiang , Gao Peng , Zhang Ligang , Huang Yuchuan , Sun Wencun , Zhang Panpan
Published: October 2021

Screw pumps have been widely used in many oilfields to lift the oil from wellbore to ground. The pump failure and delayed repair means well shut and production loss. A deep learning model is constructed to quickly identify the working status and accurately diagnose the failure types of the screw pumps, which can help the workers always get the information and give a fast repair.

Firstly, running parameters of the screw pump, such as electric current, voltage, and instantaneous rate of flow, are obtained through the Real-time Data Acquisition System. Then the correlations between values or trends of those parameters and working status of the screw pump are calculated or analyzed. Results show that there is a good correlation between the current characteristics and various working status of screw pump. Current data at different times are expressed in polar coordinates, with the polar diameter representing the current value and the polar angle representing the time. The current-time curves of massive oil wells are then plotted in images with fixed resolution and divided into nine different groups to correspond to nine frequent working status of screw pump. A convolutional neural network (CNN) model is initialized, with the current-time curve as its input and the number codes representing working status as its output. Images mentioned above are used to train the CNN model, and the model parameters, such as the number of convolution layers, the size of convolution kernels and the activation function are optimized to minimize the training losses, which are the differences between the output codes and the right codes corresponding to the images.

Finally, a robust CNN model is established, which can quickly and accurately judge the working state of the screw pump through electric current data. Based on this model, a software system connected with the oilfield database is developed, which can obtain the running parameters of the screw pumps in real time, identify their working states, judge the fault types of the abnormal situations, give alarms, and put forward solution suggestions. The system has now been widely used in Shengli Oilfield, which can help staff know the working conditions and fault types of abnormal wells in real time, speed up the maintenance progress, shorten the pump shutdown time and improve the production.
Comprehensive Strategies to Maximising Value of Late Life Assets: Lessons Learned from Mahakam Block
Authors: Rau Irfan Taufik , Herwin Henricus , Widyoko Bhayu , Hayati Iswahyuni Fifthana
Published: October 2021

Mahakam Block has been in operation for nearly half a century with cumulative production of approximately 20 trillion cubic feet of gas and 1.5 billion barrels of oil. Mature field challenges have become more evident as portrayed by declining production, more complex surface constraints, more challenging profitability of new projects and decreasing resources of new wells, which are also exacerbated by external factors such as volatility of oil and gas prices. Despite the aforementioned challenges and complexity in terms of operating numerous fields with different characteristics, Mahakam is currently still one of the biggest producing blocks in Indonesia. The success of sustaining production and prolonging the life of Mahakam is the result of continuous innovations, improvements and optimizations on various aspects over the years.

Subsurface innovative ideas by restudying and redefining geological concepts has led Pertamina Hulu Mahakam (PHM) to drill step-out wells in Handil, Tunu, South Mahakam and Sisi Nubi fields that deliver positive results and open new opportunities. In the non-subsurface aspect, Indonesia's first Plan of Development that combines higher and lower value projects across fields called OPLL (Optimasi Pengembangan Lapangan-Lapangan) was initiated in order to develop fields with marginal value and to achieve economy of scale. Moreover, Capital Expenditure (CAPEX) optimization through evolution of platform design, well architecture and sand control method is crucial for exploitation of targets with lower resources over time. PHM has also launched CLEOPATRA (Cost Effectiveness and Lean Operations in Mature Asset), later renamed to LOCOMOTIVE-8 (Low Operations Cost of Mahakam to Achieve Effectiveness and Efficiencies), to achieve Operating Expenditure (OPEX) efficiency through various initiatives driven by each entity. Due to cost of money, budget accuracy is as important as expenditures reduction meaning that more detailed and deterministic budget estimation is necessary. In addition to optimizing cost structure, PHM strives to carry out gas commercialization efforts to improve revenue streams.

In this rapidly changing era, especially for Mahakam, paradigm shift becomes highly critical. Changes in the structure and size of organization is essential to adjust with business dynamics. Adaptive organization structure is performed through digitalization and competency improvement to reduce repetitive tasks and increase productivity per capita. Cooperation between neighboring companies brings mutual benefit by sharing rig, transportation means, and pipeline network systems. Mutual benefit opportunity is also available between the company and Indonesian government by amendment of fiscal terms with the aim to enable the execution of sub-economic projects. Ultimately, one effort alone may be insignificant, but the combination of all of the efforts will be the key to the continuation of Mahakam story.
Pre-Treatment Experimental Study of Organic Acid: An Alternative Means to Overcome Inorganic Scale Build-Up Problem in Deep Well
Authors: Nasution Bagus Muliadi , Yonathan Andrew , Abdillah Muthi , Zhen Wang
Published: October 2021

Organic acid has been widely applied for inorganic scale treatment in oil and gas industry including well stimulation and scale inhibitor. Thanks to its low corrosivity and slower reaction rate with rock, organic acid is considered to offer better performance comparing to strong acid - Hydrochloric Acid (HCl). Yet, proper treatment requires vigorous analysis and experiment in order to meet foremost expectations. Besides, mistreatment of scale could result in formation damage including clay precipitation.

Pre-treatment experiments were performed on Zelda field at South East Sumatera block, that has faced with scale problem for ages. Water sample was taken from flowing Zelda A-08 well to be analyzed for mineral's saturation level. Scale was extracted from three sources including tubing, sand bailer, and Electrical Submersible Pump (ESP) of Zelda A-08. Those scale were treated in X-Ray Powder Diffraction (XRD) for mineral composition, and solubility test that utilized two types of acid system - formic acid (HCOOH) and hydrochloric acid (HCl) for comparison. Anti-swelling test and corrosion test were performed to examine the effectiveness of clay stabilizer and corrosion inhibitor.

As for carbonate analysis, both formic acid 9% and HCl 15% have comparable solubility (98.17% vs 98% for tubing's scale, 91.86% vs 82.79% for ESP's scale, and 70.30% vs 68.07% for sand bailer's scale). Yet, longer reaction is carried out by formic acid 9% (1 hour) comparing to HCl 15% (18 minutes). For silicate analysis, HF-formic acid provided the higher solubility than HF-HCl (8.34% vs 5.67% for ESP's scale and 30.48% vs 25.68% for sand bailer's scale). On anti-swelling test, by reducing swelling tendency up to 62.6%, it proves that examined clay stabilizer works perfectly against swelling potential of clay, despite of high swelling tendency of sand bailer's scale (25.8%). On corrosion test, adding on corrosion inhibitor (pyridine-based) into solution results in regular HCl 15% has corrosion rate 26.279 g/m2.h which is much higher (300%) than HF-HCl (7.977 g/m2.h) and HF-formic acid (8.229 g/m2.h). Based on pre-treatment test, formic acid 9% together with examined corrosion inhibitor and clay stabilizer, can be used as an alternative to regular HCl 15% for stimulation purpose where more areas will be covered that previously left unreachable by regular acid 15%. In addition, potentially more effective squeezed scale inhibitor using organic acid can also be achieved by performing further experiments.

The method presented in this paper for pre-treatment experimental studies of organic acid can provide engineers with intensive guide to meet the best result of organic acid treatment.
An Expert Software System has Accomplished Full Implementation of Well Integrity Management System WIMS Resulting to Excellent Safety and Production Sustainability for Critical Sour HPHT Field in Offshore Madura Strait, Indonesia
Authors: Ramadhani Fianti , Nurdin Syaiful , Etuhoko Michael Olu , Zhi Yang , Mulyono Sugeng , Mintardja Arvin , Dawson Marc
Published: October 2021

Four high-pressure-high temperature (HPHT) and sour gas wells are currently operating at Madura offshore as the only productive assets for Husky-CNOOC Madura Limited (HCML). Each well performance is very crucial to fulfill the demand of the gas customers in East Java, Indonesia.

Since starting production in 2017, the wells experienced two main well integrity challenges, high annulus pressure and wellhead growth. Both challenges are very dependent to the well flow rate and the flow duration. A continuous operation monitoring is highly required in order to keep the wells operating safely.

To overcome the challenges, HCML established a Well Integrity Management System (WIMS) document that approached several international standards as its basis. As company grows, development plan challenged the WIMS to perform faster and more efficient as compared to the existing manual system. From there, the journey of WIMS digitalization began. The journey started with the alignment of the existing WIMS document to the ISO-16530-1 at Operational Phase with more stringent boundary to operate the wells safely. The alignment covers, but not limited to the organizational structure, well barriers and criteria, monitoring and surveillance, annulus pressure management, and maintenance. The document also covered risk assessment and management of well integrity failure, which was the backbone of the WIMS digitalization.

The current digital solutions allow production data to be accessed and retrieved directly from the system for analysis purposes. It compares the recorded data with pre-determined rules and parameters set in the system. It triggers a notification to the responsible personnel to perform the required action should any anomaly occurs. It also can send a reminder to users to schedule and complete a well Integrity test to ensure that a well is always in compliance with the WIMS. All test reports and documentation are stored in the system as preparation for any future audit.

A key requirement of the expert software system was access to future developments that can offer enhanced functionality of the well integrity platform through additional near time capabilities such as predictive erosion and corrosion for downhole flow wetted components. This is being developed to enhance workover scheduling for existing wells and material selection for new wells and is planned to update automatically critical well integrity criteria such as tubing burst, collapse and MAASP.
Casing Integrity Evaluation in Complex Subsurface Intervention Abandonment Wells Using Magnetic Ranging
Authors: Rassadkin Georgy , Ridgway Douglas , Dorey Jamie
Published: October 2021

This paper describes how active and passive magnetic ranging logging used while drilling subsurface intervention wells shows characteristics of the target well casing integrity and damage.

Over the course of the development of a novel active magnetic ranging system and through several years of commercial application, data has been collected and analyzed to understand the characteristics of casing damage. This paper explains the methods used in field operations to collect this data. Using the gathered information, various stages of casing damage and poor integrity are shown.

Results obtained from active and passive magnetic ranging are presented in the context of identifying casing damage. This is a departure from the standard methods of interpreting the data as it is not focused on locating a wellbore but determining the integrity of the casing. Casing integrity in idle wells is usually understood by conventional logging techniques until there is a restriction or damage on the well. Magnetic ranging logging performed during the intervention to abandon these wells can give an indication to operators of the casing integrity that otherwise would have been unknown without access to the damaged well. This can help optimize subsequent abandonment procedures as well as assist with field planning into the future to mitigate issues stemming from casing integrity and to identify the causes of previously unknown critical casing damage.

The paper reports surface experimental data and compares it with two field examples. In the first field example, the passive magnetic interference from a hundred-year-old casing in the offset well caused more than 100000nT deviation from the reference field approximately 1ft away from the offset well, suggesting severe casing damage. The active magnetic signature measured simultaneously approaches zero, pointing to a lack of electrical continuity in the offset casing caused by a complete break. The second field example shows an offset well segment with passive interference of 7000nT in the presence of a stable active magnetic signal at approximately 2ft separation between wells due to possible casing damage without complete separation. The passive interference increases to 14000 nT at deeper depth while the active signal approaches zero due to a complete casing break.

Novel application using the data collected by active and passive magnetic ranging techniques is being applied for the understanding of issues related to casing integrity.
Prediction of Perforation Efficiency in Gas Well Using Machine Learning
Authors: Tejo Sukotrihadiyono , Amega Yasutra , Dedy Irawan
Published: October 2021

The efficiency of perforation is an important aspect in gas well since it affects near wellbore pressure drop related to turbulent flow. The perforation efficiency is correlated with non-Darcy skin that is able to be distinguished by pressure transient analysis of isochronal test (Swift et al., 1962), or evaluated from multi-rate flow test data plot coefficients (Jones et al., 1976), or type curve of single build up test following constant-rate production (Spivey et al., 2004). A simple single rate pressure transient analysis which is supported by parameters derived from historical multi rate test data was also proven to differentiate skin damage and non-Darcy skin (Aminian et al., 2007). Unfortunately there are trade-offs between accurateness and analysis time in these aforementioned methods.

Quick analysis of perforation efficiency is often needed during well completion and workover activities, to decide whether re-perforation job is required or not. To overcome the challenges of limited time for data acquisition and evaluation, an empirical relation between actual perforation length, skin damage, and laminar-turbulence flow coefficients that are obtained from short-time multi rate test is important to predict the perforation efficiency.

The empirical relation will be developed using machine learning. A simple gas reservoir model is built and then run with variations of reservoir permeability, perforation interval length, near wellbore permeability, and vertical anisotropy to generate large numbers of hypothetical multi rate test data. The data set of laminar coefficient, turbulence coefficient, absolute open flow, skin damage, and perforation length will then be trained and tested to create empirical relation using supervised regression method which will afterwards be applied to several actual field cases.

This study will elaborate the development of empirical relation of perforation efficiency with the distinct parameters obtained from simple short-time multi rate test data, what other factors will influence the empirical relation, as well as become the possible condition limit of the field application of the developed empirical relation.
Success Story of Optimizing Hydraulic Fracturing Design at Alpha Low-Permeability Reservoir
Authors: Prasetio Mario Hadinata , Anggraini Hanny , Tjahjono Hendro , Pramadana Aditya Bintang , Akbari Aulia , Madyanova Mutiara , Mekarsari Rinie , Akbarizal Andri , Setiawan Prianto
Published: October 2021

This paper describes the evolution of the hydraulic fracturing approach and design in the Alpha reservoir over the past years. Alpha reservoir in XYZ field is a laminated sandstone reservoir with low permeability in the range of 20 to 140 md at a depth of approximately 4,000 to 4,500 ft true vertical depth (TVD). XYZ field is located in Rokan block, Riau, Central Sumatra region.

Due to Alpha reservoir's nature, producing from this reservoir commercially requires stimulation. Hydraulic fracturing has been applied as the selected stimulation method to increase productivity from this reservoir. However, several challenges were recognized during the initial period, such as depleted reservoir pressure, indication of fracture height growth, and low to medium Young's modulus, which leads to few screened-out cases as well as low production gain after the fracturing treatment.

The fracturing job in Alpha reservoir has been applied since 2002. However, pressure depletion was observed through this time until waterflood optimization started in May 2018 by converting commingled injection to injection dedicated to the Alpha reservoir. The pressure responded and increased from 350 psi to approximately 800 psi. Hence this reservoir still cannot be produced in single completion without the hydraulic fracturing job due to laminated reservoir and low-permeability character.

A detailed look at the mechanical earth model (MEM) was done to revise the elastic properties and stress profile considering reservoir pressure change. The revised model was later used as an input for fracture geometry simulation. Calibration injection tests were performed and analyzed prior to the main fracturing treatments to determine fracture closure pressure and leakoff characteristics, which led to fracturing fluid efficiency. Results of these tests were used in job modifications regarding pad percentage, fracturing fluid rheology, proppant volume, and proppant concentration. Pressure history matching both after fracturing and in real time as well as the temperature log were used to validate the MEM and fracture geometries. Each change, approach, and impact were documented and statistically analyzed to determine a generic trend and design envelope for the Alpha reservoir.

Between 2019 and 2020, nine wells were stimulated that specifically targeted the Alpha reservoir, with continuous improvement in fracturing design and geomechanics properties with each well. After fracturing, the 30-day oil recovery was superior, higher than previous fractured wells, reaching more than 255 BFPD on average. The successful development of the Alpha reservoir with hydraulic fracturing led to further milestones to maximize oil recovery in XYZ field.
Turnaround Procedures to Ensure Personnel Safety During Covid-19: Best Practices and Case Study
Authors: Daher Elie , McCallum Shayne
Published: October 2021

As industry returns to work and restarts planning and execution of turnarounds and other maintenance projects, there will be regulatory, corporate and social responsibilities in order to safely restart and sustain activities. Major maintenance events and projects already bring complex challenges as multiple activities are carried out by numerous personnel and contractors, in a short duration of time. This risk is further amplified with the additional restrictions, challenges and complications posed by COVID-19.

The key challenge for turnaround managers in current times is to ensure the safety of their personnel while delivering a successful turnaround. Through a detailed analysis and breakdown of the various stages of a turnaround, this paper attempts to answer one core question- How do you keep onsite personnel safe during turnarounds in the COVID-19 era?

United Safety has developed a series of planning and prevention tools to help projects identify and implement robust actions that prevent the spread of COVID-19 onsite. These steps have been successfully implemented on site during a turnaround for a major Client in Canada. From additional precautions to new operating procedures, modifications were made at various stages including pre-planning, during the turnaround and post-turnaround stage to ensure the safety of onsite personnel.

The result was an incident-free turnaround with zero recorded transmissions on site and a comprehensive COVID-19 Turnaround Response Plan.
A Closer Look at Hydraulic Fractures: Case Studies of Microseismic Results from Horizontal Monitoring Wells
Authors: Gao Zhanwu , Li Kai , Gao Guanghui , Liao Haiyu , Zhang Yadong , Huang Zheyuan , Chen Yanyan , Liu Yuan , Li Yuanhua , Shi Ning
Published: October 2021

Amongst different options of hydraulic fracture geometry detection or measurement, microseismic monitoring is a commonly used method to reveal the hydraulic fracture geometry in three-dimensional space. Microseismic monitoring typically requires one or several monitoring wells within an effective range from the treatment well, in which the geophones are set to detect the microseismic events occurring during or after the treatment. In the past, most of the monitoring wells have been vertical wells. We present several recent case studies in which both the treatment and monitoring wells were horizontal wells, which produced some unique and interesting observations beyond the initial expectations.

One of the prerequisites of a proper microseismic monitoring of hydraulic fracturing treatment is to place the geophone in the proper position because a long distance between the actual fracturing events and the geophone may result in signal deterioration, which influences the processing and increases the uncertainty. This problem is more severe if the treatment well is a horizontal well because the distance from the geophone to the microseismic events varies between stages. One of the methods to solve this issue is to monitor the microseismic events in a horizontal offset well. As horizontal wells are often batched drilled in clusters for tight or unconventional resource nowadays, the availability of the monitoring well is less of a problem, and the constant distance from the monitoring well to the treatment well may help to generate better data quality and more accurate interpretation result.

We implemented horizontal well monitoring in two difference cases between 2018 and 2019. For case A, one horizontal monitoring well was used to monitor 54 fracturing stages in three offset wells, and for case B, we monitored 24 fracturing stages in one offset well. In both cases, the geophone arrays were shifted in multiple positions to fit the distance requirements, and both cases generate satisfying interpretation results.

The microseismic results from the two cases showed less uncertainty and better precision of microseismic events after processing, as we expected. What is surprising is this type of monitoring showed a unique physical phenomenon a couple of times, which is a casing background noise indicating excessive fracturing extension over a long distance. This phenomenon was captured in both cases, even with small injection rate and fluid volumes, which can be important information for us to better understand the dynamics of fracture propagation in such geomechanical environment and help to set a new guideline and design reference in the same region.
Knowledge Management Best Practices and Application in Field Development, IORs and Life-Cycle Reservoir Management
Authors: Lu Xiaoguang
Published: October 2021

This paper presents a unique E&P knowledge management system which has been widely accepted and applied by upstream petroleum industry. This knowledge management system started in mid-1990s and consists of standard static and dynamic knowledge base, comprehensive evaluation reports, and fit-for-purpose analytics tools applicable to the entire E&P lifecycle. Emphasis is placed on illustrating the breadth and depth of the E&P knowledge and advanced analytics in terms of their capturing and applications in field development and production.

This knowledge base consists of >1600 reservoirs from around the world, each containing ~400 reservoir-level static parameters and a set of dynamic performance data. The static parameter covers reservoir characteristics, fluid properties, original in-place volume, EUR, recovery factor, production-related data (such as well spacing, well pattern, well EUR et al.), reservoir management practices, and key IORs/EORs and their incremental recovery. The knowledge extraction process involves collecting, reviewing, and synthesizing geologic, reservoir engineering and production data on a representative sample of global reservoirs. The reliable, coherent, high-quality knowledge base provides a foundation for the development of primary recovery index using supervised machine learning. Insights and intelligence derived from this knowledge base are critical to decision-making for both initial or early field development and production stages. The development application includes, but not limited to: (1) quantifying in-place volume, EUR, and recovery factor; (2) characterizing possible production performance and uncertainties and obtaining a conceptual production performance curve; (3) validating development plan options; and (4) benchmarking reservoir simulation results. The production application includes: (1) benchmarking production performance; (2) identifying upside potential and improved oil recovery opportunities; (3) finding best practices and lessons learned in reservoir management and secondary recovery practice; and (4) screening EOR methods, calibrating potential incremental recovery and characterizing EOR process performance.

Lack of knowledge standardization and absence of coherence of data from various data sources are the main challenges facing industry's data-driven application. The knowledge management system presented in this study provides the most reliable knowledge base, advanced analytics tools, and practical application workflow to help the upstream industry become more efficient in applying collective human intelligence.
A Digital Oilfield Comprehensive Study: Automated Intelligent Production Network Optimization
Authors: Naufal Aulia Ahmad , Metra Sabrina
Published: October 2021

Production optimization on a network level has been proven to be an effective method to maximize production potential of a field with low capital. But as it stands, it is a heavy process to start along with its several challenges such as data quality issues, tedious plus repetitive work processes to deploy and reuse a complete network model. Leveraging technologies from a flow assurance simulator, python Application Programming Interface (API) toolkit, open-source machine learning packages in python, and a commercial visualization dashboard, this paper proposed a series of workflows to simplify model deployment and set up an automatic advisory system to provide insight as a mean to justify an engineer’s day to-day engineering decision.

A total of three steps was prepared to achieve field-level automated optimization system. First, is the creation of digital twin of well and network model. To eliminate potential data errors, reduce time consumed, and to merge various part of the model into one, a scalable python script was made. Second, an automated calibration workflow is created as performance issues also arises for individual branch calibration matching. Hence a combination of technologies was utilized to automate daily data acquisition and model update from production database and run a supervised machine learning model to continuously calibrate the network model. The last one is creating the customizable optimization workflow based of field KPIs, which results are derived from daily optimization run. The results are available in a personalized network surveillance dashboard accessible for engineers to create rapid decisions.

From the first and second steps, time consumed was reduced from 30 minutes/well to 10 minutes/well in bulk well modelling workflow and from 2 hours to 10 minutes for the network model merge with the assumption of 100 wells in one network. It would also greatly increase data integrity and consistency issues as it eliminates wearisome input process. On the last step, the model was successfully updated with the latest production data and the well IPRs’ Liquid PI, reservoir pressure, and holdup factor are predicted from ML with more than 90% accuracy. As result delivery, the surveillance dashboard will be populated daily with the network production data, flowing parameters, and operation recommendations. It is estimated more than 90% time is saved from manual individual runs to digital comprehensive optimization.
Application of Machine Learning Algorithms for Managing Well Integrity in Gas Lift Wells
Authors: Ragab Adel Mohamed Salem , Yakoot Mostafa Sa’eed , Mahmoud Omar
Published: October 2021

Well integrity (WI) impairments in oil and gas (O&G) wells are one of the most formidable challenges in the petroleum industry. Managing WI for different groups of well services necessitates precise assessment of risk level. When WI classification and risk assessment are performed using traditional methods such as spreadsheets, failures of well barriers will result in complicated and challenging WI management, especially in mature O&G fields.

Industrial practices, then, started moving toward likelihood/ severity matrices which turned out later to be misleading in many cases due to possibility of having skewness in failure data. Developing a reliable model for classifying level of WI impairment is becoming more crucial for the industry. Artificial intelligence (AI) includes advanced algorithms that use machine learning (ML) and computing powers efficiently for predictive analytics.

The main objective of this work is to develop ML models for the detection of integrity anomalies and early recognition of well failures. Most common ML algorithms in data science include; random forest, logistic regression, quadratic discriminant analysis, and boosting techniques. This model establishment comes after initial data gathering, pre-processing, and feature engineering. These models can iterate different failure scenarios considering all barrier elements that could contribute to the WI envelope. Thousands of WI data arrays can be literally collected and fed into ML models after being processed and structured properly.

The new model presented in this paper can detect different WI anomalies and accurate analysis of failures can be achieved. This emphasizes that managing overall risks of WI failures is a robust and practical approach for direct implementation in mature fields. It also, creates additional enhancement for WI management. This perspective will improve efficiency of operations in addition to having the privilege of universality, where it can be applicable for different well groups. The rising wave of digitalization is anticipated to improve field operations, business performance, and production safety.
Artificial Intelligence Method of Flow Unit Division Based on Waveform Clustering: A Case Study on Zhetybay Oil Field, South Mangyshalk Basin, Kazakhstan
Authors: Fu Libing , Ni Jun , Liu Yuming , Li Xuanran , Xu Anzhu
Published: October 2021

The Zhetybay Field is located in the South Mangyshlak Sub-basin, a delta front sedimentary reservoir onshore western Kazakhstan. It was discovered in 1961 and first produced by waterflooding in 1967. After more than 50 years of waterflooding development, the reservoirs are generally in the mid-to-high waterflooded stage and oil-water distribution becomes complicated and chaotic. It is very difficult to handle and identify so much logging data by hand since the oilfield has the characteristics of high-density well pattern and contains rich logging information with more than 2000 wells. The wave clustering method is used to divide the sedimentary rhythm of the logging curve. Sedimentary microfacies manifested as a regression sequence, with four types of composite sand bodies including the composite estuary bar and distributary channel combination, the estuary bar connected to the dam edge and the distributing channel combination, the isolated estuary bar and distributing channel combination, and the isolated beach sand. In order to distinguish the flow units, the artificial intelligence algorithm-support vector machine (SVM) method is established by learning the non-linear relationship between flow unit categories and parameters based on developing flow index and reservoir quality factor, summarizing permeability logarithm and porosity degree parameters in the sedimentary facies, and analyzing the production dynamic. The flow units in Zhetybay oilfield were classified into 4 types: A, B1, B2 and B3, and the latter three are the main types. Type A is distributed in the river, type B1 is distributed in the main body of the dam, type B2 is mainly distributed in the main body of the dam, and some of B2 is distributed in the dam edge, and B3 is located in the dam edge, sheet sand and beach sand. The results show that the accuracy of flow unit division by support vector machines reaches 91.1%, which clarifies the distribution law of flow units for oilfield development. This study is one of the significant keys for locating new wells and optimizing the workovers to increase recoverable reserves. It provides an effective guidance for efficient waterflooding in this oilfield.
Probabilistic Dynamic Modelling and Prediction Workflows: Application to Multi-Layered Waterflood Reservoir with 90 Years Production History and 293 Wells
Authors: Noviyanto Ecko , Rohman Deded Abdul , Nopranda Theoza , Simanjorang Rudini , Haro Kosdar Gideon , Utomo M. Isa Priyo , Prasetyo Arif Bagus , Pratama Andi
Published: October 2021

This paper presents a probabilistic modeling and prediction workflow to capture the range of uncertainties and its application in a field with many wells and long history.

A static model consisting of 19 layers and 293 wells was imported as the base model. Several reservoir properties such as relative permeability, PVT, aquifer, and initial condition were analyzed to obtain the range of uncertainties. The probabilistic history matching was done using Assisted History Matching (AHM) tools and divided into experimental design and optimization. The inputted parameters and their range sensitive to objective functions, e.g., oil rate/total difference, could be determined using a Pareto chart based on Pearson Correlation during experimental design. The optimization phase carried over the most sensitive parameters and utilized Particle Swarm Optimization (PSO) algorithm to iterate the process and find the equiprobable models with minimum objective functions.

After filtering a set of models created by AHM tools by the total oil production, field/well oil objective functions, the last three years' performance, and clustering using the k-means algorithm, there are 11 models left. These models were then analyzed to understand the absolute risk and parameter uncertainties, e.g., mobile oil or sweep efficiency. Three models representing P10, P50, and P90 were picked and used as the base models for developing waterflood scenario designs. Several scenarios were done, such as base case, perfect pattern case, and existing well case. The oil incremental is in the range of 1.60 – 2.01 MMSTB for the Base Case, 7.57 – 9.14 MMSTB for the Perfect Pattern Case, and 6.01 – 7.75 MMSTB for the Existing Well Case.

This paper introduces the application of the probabilistic method for history matching and prediction. This method can engage the uncertainty of the dynamic model on the forecasted production profiles. In the end, this information could improve the quality of management decision-making in field development planning.