bluuebunny/crossref_metadata_2025 huggingface dataset:
Novel Drill Bit Analysis to Delay and Mitigate Fixed Cutter Thermal Damage
Authors: Roberts Sion Llywelyn , Bailey Michael James , Babaie Aghdam Afshin , Suleiman Ahmed , Fathy Ahmed
Published: October 2021

As oil and gas wells become deeper, drilling longer intervals is becoming a major milestone for drill bit companies, as the process comes with a variety of challenges affecting the durability of drill bits. Among the major challenges are thermal and impact damage in polycrystalline diamond compact (PDC) cutters, which can significantly affect the performance and longevity of a drill bit. While cutter technology development remains an important arena to address said challenges, there exists a need to also address these through the design process. This paper presents the development and deployment of a new drill bit analysis method that addresses thermal damage by optimizing the design, which has been field validated across the globe.

The analysis involves estimating the thermal input load and the available cooling rate for every cutter on a drill bit during drilling conditions. The data is then used to optimize and apply changes to the design. The analysis considers all the critical and relevant operational parameters to calculate these indices. The outcome of the so-called thermal index analysis enables the design team to make informed decisions to improve the design of the drill bit and to minimize the extent of thermal damage in cutters.

The improvements made in the design include changes in cutting structure to affect cutting forces and, eventually, the thermal input load during the drilling process. This stage in practice can bring down the temperature of the cutting edge by 20%, as calculated analytically. Another major change that can affect the results is hydraulic design of the bit, which includes the location of the nozzles as well as their orientation and size. In test cases, the cooling rate improved by 50% while keeping the same flow rate though the bit.

Several field trials have validated the correlation of thermal index analysis to drill bit dulls. This analysis is now in the field evaluation and testing phase, where it is being used during the design process to improve bits with thermal damage. The field-testing phase has been primarily conducted in thermally challenging applications across the Middle East, North Africa region, and in West Texas.
Short Term Injection Re-Distribution STIR: Real-Time Waterflood Optimization Technique Using Advanced Data Analytics
Authors: Modi Gaurav , Ujjwal Manu , Simha Srungeer
Published: October 2021

Short Term Injection Re-distribution (STIR) is a python based real-time WaterFlood optimization technique for brownfield assets that uses advanced data analytics. The objective of this technique is to generate recommendations for injection water re-distribution to maximize oil production at the facility level. Even though this is a data driven technique, it is tightly bounded by Petroleum Engineering principles such as material balance etc. The workflow integrates and analyse short term data (last 3-6 months) at reservoir, wells and facility level.

STIR workflow is divided into three modules:

Injector-producer connectivity Injector efficiency Injection water optimization

First module uses four major data types to estimate the connectivity between each injector-producer pair in the reservoir:

Producers data (pressure, WC, GOR, salinity) Faults presence Subsurface distance Perforation similarity – layers and kh

Second module uses connectivity and watercut data to establish the injector efficiency. Higher efficiency injectors contribute most to production while poor efficiency injectors contribute to water recycling.

Third module has a mathematical optimizer to maximize the oil production by re-distributing the injection water amongst injectors while honoring the constraints at each node (well, facility etc.) of the production system.

The STIR workflow has been applied to 6 reservoirs across different assets and an annual increase of 3-7% in oil production is predicted. Each recommendation is verified using an independent source of data and hence, the generated recommendations align very well with the reservoir understanding. The benefits of this technique can be seen in 3-6 months of implementation in terms of increased oil production and better support (pressure increase) to low watercut producers. The inherent flexibility in the workflow allows for easy replication in any Waterflooded Reservoir and works best when the injector well count in the reservoir is relatively high. Geological features are well represented in the workflow which is one of the unique functionalities of this technique. This method also generates producers bean-up and injector stimulation candidates opportunities.

This low cost (no CAPEX) technique offers the advantages of conventional petroleum engineering techniques and Data driven approach. This technique provides a great alternative for WaterFlood management in brownfield where performing a reliable conventional analysis is challenging or at times impossible. STIR can be implemented in a reservoir from scratch in 3-6 weeks timeframe.
A New Method of Carbonate Matrix Stimulation Modelling and Optimisation Using Field Experimental Data for Acid Efficiency Calibration and Case Studies in Kazakhstan
Authors: Kalabayev Ruslan , Abdrazakov Dmitriy , Chuprakov Dmitry
Published: October 2021

Successful carbonate matrix acidizing treatments require addressing pay rock mineralogy, produced fluid flow profile, selection of the best stimulation fluids, and correct placement of these fluids. A unique method of acid and diverter fluid efficiency calibration using field experimental data for treatment modelling and optimization has been implemented successfully in several mid-temperature reservoirs, including giant oil fields in Kazakhstan. Application of the technique led to positive results.

Matrix stimulation is selected as the primary method for raising production from many carbonate reservoirs in the region because of the reservoir features. Coreflood testing conducted with candidate acid systems for selection and optimization of treatment fluid formulations and design schedules did not always lead to the desired post-stimulation skin levels, zone coverage, and production results. Hence, large-scale calibration of the acid parameters to the actual reservoir conditions was attempted. Treatment modelling in an advanced matrix acidizing software considered calibration factors derived from field tests. Thereafter, the optimized designs were implemented in the same reservoirs to improve the incremental production.

Whenever possible, coreflood testing was carried out as the first step to determine the pore-volume to breakthrough parameters for the candidate acid systems. As the second step, these laboratory-derived data were used for modelling of the offset well stimulation design. Third, the actual treatment downhole pressure was matched with the simulated pressure by means of acid efficiency calibration in the matrix stimulation software. These calibrated parameters were then used for simulation of the following treatments in the same formation in attempt to model the expected reservoir placement and zone coverage more accurately and realistically to maximize the treatment effect. Post-stimulation fluid flow profile surveys have validated the optimized models and applicability of the methodology for improving incremental well productivity in the subject reservoirs.

The stimulation approach uses a unique technique of acid efficiency calibration using field experimental data. It requires good knowledge of reservoir lithology and permeability and porosity profiles in the target zones. The initial skin is calibrated using pretreatment production data. Thereafter, acid efficiency is calibrated based on matching the actual stimulation job pressures.
Successful Wellbore Pressure Management Using Intelligent MPD and Continuous Circulation System on an HPHT Well in Vietnam
Authors: Ta Quoc Bao , Dalgit Singh Harpreet Kaur , Nguyen Le Quang Tuan , Nguyen Van Dien , Sammat Essam
Published: October 2021

A managed pressure drilling (MPD) and early influx detection system is gaining worldwide acceptance as an enabling technology for drilling wells with challenges that can lead to tremendous nonproductive time (NPT), significant unplanned costs, and increased risk exposure. MPD counteracts the high cost of these wells by delivering significant savings when eliminating fluid losses or well control events that cause NPT. MPD technology has proven that is used to not only reduce NPT but also enable access to reserves previously considered un-drillable. In this case history, MPD helped to reach reserves that could not be reached in the first well.

Client planned to drill the well A, which is its second offshore exploration well. Early on in 2019, the campaign encountered significant problems because of high temperatures and a narrow pore-pressure/fracture-pressure (PP/FP) gradient window. Additionally, using conventional drilling methods in offset wells led to problems relating to kicks, loss scenarios, and stuck pipe. Before drilling the second exploration well, the relevant parties considered that the first well-presented multiple drilling issues, and they drew from past success. The latter job had ended with reaching all the well targets despite high-pressure/high-temperature (HP/HT) conditions using a continuous circulating device in conjunction with an MPD system. Therefore, this combination of technologies was chosen to drill the well A.

The operator used the MPD system, from the start when drilling the 14 3/4-in × 16-in. hole section to the end when drilling the 8 1/2-in. hole section, in offshore Vietnam. Applying MPD technology on this well resulted in many benefits, including the main benefit of always controlling the bottomhole pressure through the challenging zones. MPD also helped to maintain the equivalent circulating destiny (ECD) and equivalent static density (ESD) during drilling, connections, and a logging operation to mitigate the risk of any gas breaking out at the surface and to drill the well to the desired target depth.

This paper focuses on using MPD technology in conjunction with the continuous circulation system, in offshore Vietnam. It goes into detail by describing the experience and providing some of the lessons learned.
An Application of Probabilistic Risk Assessment PRA Methodology in the Optimization of CO2 Injection Well Locations for CO2 Storage Development Plan, Offshore Malaysia
Authors: Widyanita Ana , Cai Zhong , Abdul Hamid Mohd Khaidhir , Jones Ernest A
Published: October 2021

A depleted gas field in the Sarawak basin, offshore Malaysia, was selected as a candidate geological site for CO2 storage. The selection and design for the CO2 injection well locations are an important decision making in the business planning, which involved a complicated risk assessment system covering subsurface and surface integration. The objective in this paper is to apply the probabilistic risk assessment method in the optimization of CO2 injection well locations for the CO2 storage development plan.

Risk comes from uncertainty, so the workflow with probabilistic risk assessment (PRA) methodology includes: 1) the risk identification and sensitivity analysis: the definition of uncertainty elements; 2) the risk analysis: the uncertainty ranges and distributions; and 3) Risk evaluation: including the individual element and composite risk evaluation. Similar to risk matrix method, two parameters used: 1) the likelihood (Pi) of occurrence of each consequence, expressed by the percentile of a threshold; and 2) the severity magnitude (Ii) of the possible adverse consequences, expressed by the coefficient of variation. The total risk is the sum of the products of Pi and Ii, displayed on the risk map.

The workflow and methodology were applied in a depleted gas reservoir for CO2 storage. The main elements identified for uncertainty analysis include: 1) structural model including the top of reservoir depth and internal reservoir zonation; 2) reservoir parameters based on porosity and permeability; 3) fluid contacts. Combing all possible cases for each element, a number of scenarios of reservoir models were constructed, which are the foundation of risk evaluation.

Risk score, expressed by probability, was calculated by all elements, generating a composite risk probability map, where there are different risk levels at different locations. Combining with engineering constraints, the CO2 injection well locations were selected by optimization, avoiding the area with high risk.
Machine Learning Application to Estimate Missing Rock Mechanical Logs and Reduce Uncertainty While Drilling
Authors: Negara Ardiansyah , Magana-Mora Arturo , Khan Khaqan , Vossen Johannes , Zhan Guodong David , Otalvora William Contreras , Bomidi John
Published: October 2021

This study presents a data-driven approach using machine learning algorithms to provide predicted analogues in the absence of acoustic logs, especially while drilling. Acoustic logs are commonly used to derive rock mechanical properties; however, these data are not always available. Well logging data (wireline/logging while drilling - LWD), such as gamma ray, density, neutron porosity, and resistivity, are used as input parameters to develop the data-driven rock mechanical models. In addition to the logging data, real-time drilling data (i.e., weight-on-bit, rotation speed, torque, rate of penetration, flowrate, and standpipe pressure) are used to derive the model. In the data preprocessing stage, we labeled drilling and well logging data based on formation tops in the drilling plan and performed data cleansing to remove outliers. A set of field data from different wells across the same formation is used to build and train the predictive models. We computed feature importance to rank the data based on the relevance to predict acoustic logs and applied feature selection techniques to remove redundant features that may unnecessarily require a more complex model. An additional feature, mechanical specific energy, is also generated from drilling real-time data to improve the prediction accuracy. A number of scenarios showing a comparison of different predictive models were studied, and the results demonstrated that adding drilling data and/or feature engineering into the model could improve the accuracy of the models.
Boosting Algorithm Choice in Predictive Machine Learning Models for Fracturing Applications
Authors: Khan Abdul Muqtadir , BinZiad Abdullah , Subaii Abdullah Al
Published: October 2021

With the advancement in machine learning (ML) applications, some recent research has been conducted to optimize fracturing treatments. There are a variety of models available using various objective functions for optimization and different mathematical techniques. There is a need to extend the ML techniques to optimize the choice of algorithm.

For fracturing treatment design, the literature for comparative algorithm performance is sparse. The research predominantly shows that compared to the most commonly used regressors and classifiers, some sort of boosting technique consistently outperforms on model testing and prediction accuracy. A database was constructed for a heterogeneous reservoir. Four widely used boosting algorithms were used on the database to predict the design only from the output of a short injection/falloff test.

Feature importance analysis was done on eight output parameters from the falloff analysis, and six were finalized for the model construction. The outputs selected for prediction were fracturing fluid efficiency, proppant mass, maximum proppant concentration, and injection rate. Extreme gradient boost (XGBoost), categorical boost (CatBoost), adaptive boost (AdaBoost), and light gradient boosting machine (LGBM) were the algorithms finalized for the comparative study. The sensitivity was done for a different number of classes (four, five, and six) to establish a balance between accuracy and prediction granularity. The results showed that the best algorithm choice was between XGBoost and CatBoost for the predicted parameters under certain model construction conditions. The accuracy for all outputs for the holdout sets varied between 80 and 92%, showing robust significance for a wider utilization of these models.

Data science has contributed to various oil and gas industry domains and has tremendous applications in the stimulation domain. The research and review conducted in this paper add a valuable resource for the user to build digital databases and use the appropriate algorithm without much trial and error. Implementing this model reduced the complexity of the proppant fracturing treatment redesign process, enhanced operational efficiency, and reduced fracture damage by eliminating minifrac steps with crosslinked gel.
Unlocking ESP Potential in High Gas/Liquid Ratio Wells Using Gas Lock Prevention Control in West Java Field, Indonesia
Authors: Saputra Muhammad Nur Wangsa , Ciptaningsih Rahmi , Febryana Niken Endah
Published: October 2021

West Java field operated 60 electric submersible pumps (ESPs) as a lifting method with 2.850 BOPD production contribution. These ESP wells produce from a mature structure. At one point, 48% of ESP operation were shut down due to ESP non-optimum operation. The challenges in ESP operations in the asset are high gas-liquid ratio (GLR), impurities, sand and scale buildup, and well integrity where high GLR was deemed as the major problem that deteriorated the ESP's performance.

Conventional ESPs gas separator were installed in the field, but the gas-handling device could not handle more than 45% free gas while some wells have more than 50% free gas. Three wells in particular were assessed, Well A, Well B, and Well C, which have 585, 1196, and 1690 average GLR respectively. These wells had problem with reduced pump efficiency and very low run life due to frequent ESP trips which were caused by the gas lock problem. A solution to maintaining oil production was by changing the production zone to zone that producing less gas and by installing more advanced gas-handling device. However, the probability of experiencing high gas production from new zones can't be ruled out therefore other mitigation plan had to be found.

Gas lock protection control is an algorithm that manipulates ESP real-time rotational speed to prevent gas interference inside pump. The algorithm was introduced as a mitigation measure and commissioned in July 2020 at Well A where it directly optimized production by 16%. To prove the robustness of the gas lock prevention control, the project was then extended to Well B and Well C which began to implement gas lock prevention control in August 2020 to handle the increase of their gas production. Thanks to this gas lock prevention control, the wells have been able to maintain production without spending either time or money to change production zone or to change the ESP completion.

Going forward, gas lock protection control will be set as an option on ESP devices. Thus, unplanned gas interference effects may be reduced in other wells that being produced by ESP thereby helping to maintain production at an optimum level.
Optimal Design and Scheduling of Offshore Oil and Gas Field Development with Environmental Impact Consideration
Authors: Nugraheni Novita Dwi Putri , Li Jie
Published: October 2021

The objective of the paper is to develop a mixed integer nonlinear programming (MINLP) model for optimum design and scheduling of offshore oil and gas field development in respect to simultaneous consideration of economic and environmental impact. The model is utilized as a tool for decision making management in conceptual stage. Nonlinear reservoir behavior and floating demand constraint are incorporated to improve accuracy of the solution.

This paper utilizes mathematical programming techniques to address the design and scheduling problem of offshore oil and gas field development. Field development problem is first formulated into a multi-objective MINLP model incorporating many realistic features such as nonlinear reservoir behavior and floating demands. The objectives are to maximize net present value (NPV) and minimize total environmental impact (TEI) simultaneously. Environmental impact is assessed using the ReCiPe2016 method. Augmented ε-constraint method (AUGMECON) is then employed to solve the proposed multi-objective MINLP model to generate the Pareto-optimal front that is able to assist decision maker selecting the most preferred solution.

The performance of the proposed modelling framework is investigated on a set of problem which consists of 2 reservoirs, 2 FPSOs, 2 customers and 5-years planning horizon. First model with single objective function to maximize NPV can be solved effectively within short computational time. The solution gives optimum decision of design, investment, production schedule, and transportation regardless the environmental impact. Then, simultaneous optimization of multi-objective MINLP with different value of ε-constraint generates multiple development schemes and objective function values. The results indicate trade-off between maximizing NPV and minimizing TEI. It is possible to obtain maximum NPV of USD 2.4 trillion at the expense of TEI which is 307.518 or to generate minimum TEI of 16.65 at the expense of NPV which is USD 74.368 billion. All possible solutions within extreme values range are presented in form of a Pareto-optimal front where TEI and NPV are plotted in x and y-axis respectively. It will assist the company to select the most preferred solution based on NPV. Consequently, the selected option brings corresponding value of TEI. Additionally, the Pareto optimal front also allows decision maker to have more flexibility to compromise between economic and environmental issues.

This is the first study to consider environmental impact in the offshore oil and gas field development. Many realistic operational features such as nonlinear reservoir behavior and floating demands are also incorporated. In addition to that, the proposed framework yields a powerful tool to assist decision maker selecting the most preferred solution that satisfies their criteria in both economic and environmental aspects.
Risk Assessment and Enhanced Oil Recovery Effect of Gravity Assisted Oxygen-Reduced Air Flooding
Authors: Huang Lijuan , Li Zongfa , Ren Shaoran , Liu Yanming
Published: October 2021

The technology of air injection has been widely used in the second and tertiary recovery in oilfields. However, due to the injected air and natural gas will explode, the safety of the gas injection technology has attracted much attention. Gravity assisted oxygen-reduced air flooding is a new method that eliminates explosion risks and improves oil recovery in large-dip oil reservoirs or thick oil layers. The explosion limit data of different components of natural gas under high pressure were obtained through explosion experiments, which verified the suppression effect of oxygen-reduced air on explosions. The influence of natural gas composition and concentration on explosion limits was also investigated. In addition, a rotatable displacement device was used to study the feasibility of gravity assisted oxygen-reduced air injection for improving the heavy oil reservoirs recovery. Under pressure and temperature conditions of 20MPa and 371K, the sand-filled gravity flooding experiments with different dip angles were carried out using oxygen-reduced air with an oxygen content of 8%. The results show that with the increase of the reservoir dip, the pore volume of the injected fluid at the gas channeling point, the efficient development time of gas injection, and the final displacement efficiency of gas injection development all increase through gravity stabilization caused by gravity differentiation. In the presence of a dip angle, the cumulative oil production before the gas breakthrough point exceeded 80% of the oil production during the entire production process, indicating that gravity assisted oxygen-reduced air flooding is an effective and safe improving oil recovery method. Finally, the explosion risk of each link of the air injection process is analyzed, and the high-risk area and the low-risk area are determined.
The Challenges and Lessons Learned from the Unanticipated Long Run Well Unloading Campaign in T Field, Malaysia
Authors: W Hassan W M Faris , Thamilyanan Thivyashini , Suggust Alister , Usop M Zulfiqar , Hamdan Nurfatrina , M Razali Abdullah , Latif Nurlizawati , Bakar Hasmizah , Kumar Suman
Published: October 2021

This paper presents overall challenges and experiences that the team were facing during offline well unloading campaign recently. On paper, offline well unloading is more favorable as an option to minimize environmental impact, economize rig days and reuse produced clean-up oil. This practice is not new. The implementation of this practice to a 40-year-old brown fields led to successfully bring adopted for two drilling campaign. The well unloading to the existing facilities requires substantial strategies to be adapted and possibilities to handle drilling fluids and solid/debris are extensively discussed within multi disciplinary team whereby risk evaluation was conducted ensure the good progress of this operation. The assurance part to handle production of solid/debris along with produced fluid to surface within 12 hours operation with the assitace from Hook-Up and Commissioning (HUC) work barge initially and resumed later by daily trip via boat to offshore site will be discussed in this paper. Almost 90% of this activity is carried out with crews mobilized with 45-minute boat trip from Bintulu to site, highly depended on weather condition. This paper also will share the major challenges that resisted the good progress of unloading for four wells such as limitation associated setting of the surface Hi Lo Pilot safety switch that cannot handle the transient flow condition, huge pressure drop exerted by the inline surface strainer that temporarily installed to filter any solid production, unintentional jammed close of Tubing Retrieveable Surface Controlled Subsurface Safety Valve (TRSCSSV), unanticipated low performance of gas lift compressor that restricted the wells to continuously flow to the test separator and temporary high H2S gas release throughout the well unloading operation. Due to daily tripping, the crew left with constrained time of 4 - 5 effective hours only per day in attempts to kick off and flow the wells. On paper, offline well unloading indeed offered 70% cost saving compared to clean up with rig. However, in actual it resulted to increase 3 times in well cost and delay in achieving first production target which was 6 months later from the plan due to unanticipated causes that prolong the unloading activities.
Successful Water Shut Off Strategy for Multilayer Tubingless Wells at Mahakam Field: JM-X Case Study
Authors: Umar Khalid , Rahman Risal , Hidayat Reyhan , Kurniawati Pratika Siamsyah , Marindha Rantoe , Dahnil Gitani Tsalitsah , Pancawisna Gerardus Putra , Hidayat Danny , Az-Zariat Arasy , Utama Fia
Published: October 2021

The objective of this paper is to present the Mechanical Water Shut-Off (MWSO) strategy for multilayer reservoirs on tubingless well. With 10 open perforated reservoirs and no selectivity option, isolation on water producing reservoir will be the main challenge since production is commingled throughout the lifetime of well. Regular production tests performed through a Multiphase Flowmeter equipment on each offshore platform is a first indicator to monitor the evolution of water production in a well. JM-X well has been experiencing water breakthrough since one week after initial perforation and WGR keep increasing following gas production decline. The strategy was initiated by conducting a bottom hole monitoring survey to identify water sources. Production Logging Tool (PLT) was used to precisely monitor pressure, temperature, water holdup, and fluid rate along the wellbore for further water source and production allocation analysis. Once the water source reservoirs have been identified, MWSO operation was requested. There are several types of MWSO equipment that are commonly used in Offshore Mahakam field each of which has selective economic consideration based on the expected well reserve. Considering operation difficulties and cost, MWSO program was made then will be monitored during the operation time to ensure the operation runs safely and smoothly. MWSO strategy on well JM-X was proven to be able to reduce water production from 900 bpd to only 20 bpd with a significant gain of gas production from 3 MMscfd to 9.2 MMscfd and oil production from 200 bpd to 750 bpd.
Formation Grain Size Profile Prediction Model Considering the Longitudinal Continuity of Reservoir Using Artificial Intelligence Tools
Authors: Liu Shanshan , Wang Zhiming
Published: October 2021

Grain size characteristics (d50, UC) of formation sands are crucial parameters in a sand control design. UC and d50 are commonly derived from sieve or laser particle size analysis (LPSA) techniques on a limited number of core samples in the process of drilling, which cannot represent the variations of grain sizes in the formation by the limited number of core samples. Moreover, staged and hierarchic design of sand control usually needs the whole longitudinal distribution profile of grain size. The grain size characteristics of the reservoir are formed in the process of a long history and have a good correlation with the formation environment of the sediments. Sand control design can only use test well data, because of lacking actual producing position cores. The vertical and horizontal anisotropy and heterogeneity of reservoirs bring difficulties and greater risks to the design of sand control schemes. Therefore, it is very important to find a simple and effective reservoir granularity prediction method.

The existing prediction models by artificial intelligence method use single point logging data as eigenvalues to predict d50 and UC without considering the longitudinal continuity of data. This paper presents an efficient solution to predict grain size profile based on conventional logging curves by using four machine learning method (ANN, Random forest, XGBoost, SVM). In order to make full use of the geological continuity of the reservoir, the longitudinal continuous points according to the spatial correlation are adopted as the machine learning feature parameters from the perspective of geological analysis and the data-driven grain size profile prediction model are established by using the logging curve trend and background information, which further improves the prediction accuracy of the model and provides basic data for sand control. The ANN model of five point mapping has the best prediction effect in predicting d50 with a highest correlation coefficient 0.819 and a lowest error MAE 9.59. The XGBoost model of five point mapping has the best prediction effect in predicting UC with a highest correlation coefficient 0.402 and a lowest error RMSE 1.15. This method has been successfully used in offshore oil field in sand control optimization.
How to Land Modern Data Science in Petroleum Engineering
Authors: Zhang Hongbao , Zeng Yijin , Liao Lulu , Wang Ruiyao , Hou Xutian , Feng Jiangpeng , Mulunjkar Amol
Published: October 2021

Digitalization and intelligence are attracting increasing attention in petroleum engineering. Amounts of published research indicates modern data science has been applied in almost every corner of petroleum engineering where data generates, however, mature products are few or the performance are not up to peoples’ expectations. Despite the great success in other industries (internet, transportation, and finance, etc.), the "amazing" data science algorithms seem to be challenged when "landing" in petroleum engineering. It is time to calmly analyze current situations and discuss the methodology to apply modern data science in petroleum engineering, for safety ensuring, efficiency improvement and cost saving.

Based on the experiences of several data products in petroleum engineering and wide investigation of literatures, the methodology is summarized by answering some important questions: what is the difference between petroleum engineering and other industries and what are the greatest challenges for algorithms "landing"? how could we build a data product development team? why the machine learning models didn't work well in real world, which are derived by typical procedures in textbooks? are current artificial intelligent algorithms perfect and is there any limit? how could we deal with the relationship between prior knowledge and data-driven methods? what is the key point to keep data product competitive? Several specific scenarios are introduced as examples, such as ROP modelling, drilling parameters optimization, text mining of drilling reports and well production prediction, etc. where deep learning, traditional machine learning, incremental learning and natural language processing methods, etc. are used.

Besides detailed discussions in the paper, conclusions are summarized as: 1) the strengths and weakness of current artificial intelligence should be viewed objectively, practical suggestions to make up the weakness are provided; 2) the combination of prior knowledge (from lab tests or expert experiences) and data-driven methods are always necessary and methods for the combination are summarized; 3) data volume and solution portability are the key points to improve data product competitiveness; 4) suggestions on how to build a multi-disciplinary R&D team and how to plan a product are provided.

This paper conducts an objective analysis on challenges for modern data science applying in petroleum engineering and provides a clear methodology and specific suggestions on how to improve the success rate of R&D projects which apply data science to solve problems in petroleum engineering.
Precise Cement Displacement with a New Cement Plug Tracking Method
Authors: Kabannik Artem , Korkin Roman , Demidov Demid , Fedorov Andrey , Khudorozhkova Aleksandra , Nieczkoski Micaela
Published: October 2021

During the primary well cementing operation, when the cement slurry is pumped into the annulus around the outside of the casing string, it is very critical not to over displace and let the displacement fluid enter the annulus.

Traditionally, to determine when to stop the cement displacement operation, the top cement plug position is tracked volumetrically by dividing the displaced volume by the casing internal cross-sectional area. However, the volumetric method is prone to uncertainties related to displacement fluid compressibility, high-pressure pump inefficiency, flowmeter inaccuracy, and variance in casing joint diameters.

The new cost-effective cement displacement monitoring method is based on the analysis of the pressure pulses generated by the top cement plug passing the casing. These pressure pulses are detected by the standard pressure transducer installed at the cementing head. When correlated with the casing tally, these pulses identify the plug position related to the completion elements that provide better accuracy than the volumetric method used conventionally.

The case studies include the successful cement displacement monitoring example and the case where the plug was prematurely stopped 90 meters above the landing collar, which was revealed by the subsequent drilling and confirmed independently by the new plug tracking method.
Optimization Strategies of Production Parameters to Prevent Hydrate Reformation in Marine Gas Hydrate Production System
Authors: Liu Zheng , Sun Baojiang , Wang Zhiyuan , Zhang Jianbo
Published: October 2021

In recent decades, the development of natural gas hydrates has become a research hotspot of scholars all over the world. However, the decomposed gas and water in marine gas hydrate production system may regenerate gas hydrates due to the low-temperature and high-pressure environment in seafloor. In this study, a transient temperature and pressure calculating model was established to predict the risk of hydrate reformation in production pipelines during offshore natural gas hydrate development. Using the proposed model, the region of hydrate reformation in gas hydrate production wells were predicted quantitatively. Meanwhile, the hydrate reformation management strategies through optimization of production design parameters in combination with hydrate inhibitor injection were proposed and discussed in detail. The results indicate that the risk of hydrate reformation is the highest in the drainage pipeline (DP); however, the flow in gas-water mixed transportation and gas production pipelines (MTP and GPP) basically does not satisfy the hydrate formation condition. In the process of production well design, adding additional the EH and ESP can fully eliminate the hydrate reformation risk in the DP without using the hydrate inhibitor.
Smart Segmented Polymer Injection Pilot: A Case Study
Authors: Sun Fuchao , Pei Xiaohan , Gai Xubo , Sun Shuang , Hu Shifeng
Published: October 2021

Polymer flood is proved an effective method for EOR in China. Traditional segmented polymer injection technique cannot obtain continuous layer parameters. Real-time monitoring is necessary for polymer flood because downhole pressure and flowrate vary more often than waterflood. Existing technique for layered monitoring and flowrate adjustment is wireline test. There is no smart technique which can realize real-time monitoring and automatic flowrate control. In this paper, a smart segmented injection technique for polymer flood well is introduced. A smart distributor is permanently placed in each layer. It is composed of flowmeter, temperature sensor, two pressure sensors, downhole choke and electrical control unit. The special flowmeter is adopted for polymer flowrate test. All the distributors are connected together by a single control line which is set outside of the tubing string. Operator can read the data of each layer and adjust the flowrate whenever needed at any time which makes the technique a smart one. The smart technique for polymer flood wells has been implemented in a polymer well in Daqing oilfield of China. A case study for smart segmented polymer injection pilot is introduced in detail including technical principle, indoor test results, construction process and adjustment process. The application results show that the operator on the ground can easily obtain downhole tubing pressure, layer annulus pressure, temperature and flowrate on line. The sample time can be set to any one between 1-65536s according to geological engineer's advice. There is no limitation caused by battery power because the distributor is powered by cable on the ground. In terms of adjustment, the flowrate can be adjusted according to the target value. And it can also be regulated at any time manually, just needing pushing the mouse in the office. The application also displays that the smart segmented technique has the advantage for polymer injection because of larger change of layered parameters. It can provide more real-time data for oil development engineer and the data are beneficial for better understanding and optimization of the reservoir. Therefore, the smart segmented polymer injection has a great potential for EOR based on polymer flood.
A Novel Approach to Evaluate Deliverability of Gas Wells with Pressure Fluctuation
Authors: Zhang Yongbin , Sun Xiongwei , Bai Xiaojia , Jia Wei , Zhu Bo , Wang Haibo
Published: October 2021

Majority of gas fields in Tarim Basin are HPHT (high-pressure/high temperature) reservoirs with buried depth more than 5000m. The special geological conditions made it a challenge for underground well testing due to safety issues. Additionally, wellhead pressure fluctuation is widely existed both from geological and engineering factors, including sand production, well casing integrity problems, contamination of downhole fracturing fluid and wax deposition in wellbore etc. Traditional deliverability evaluation method which relies on underground well testing is greatly limited as it is not capable of reflecting the dynamic change of gas well deliverability due to abnormal wellhead pressure fluctuation.

In this study, a new approach is proposed to evaluate the deliverability of these kind of wells using dynamic data from wellheads. An apparent and a potential deliverability curves are based on binomial deliverability equation are established individually according to whether the additional skin caused by wellbore blockage is taken into consideration. The variation characteristic of gas well deliverability is obtained by comparison of potential and apparent absolute open flow. Finally, field studies of Dina abnormal wells are performed to verify the accuracy of the method. Deliverability analysis show that the new approach has a great advantage in evaluating the production potential of wells with pressure fluctuation, and furtherly provides the criteria for wellbore management.
Application of Due Diligence Audit for Value Creation: Case Study from Gas PSC of Malaysia
Authors: Sinha Ankaj Kumar , Lim Hue Teng
Published: October 2021

As resource owner of all hydrocarbon assets of Malaysia, Petroliam Nasional Berhad (PETRONAS) through Malaysia Petroleum Management (MPM) is responsible for providing asset integrity assurance, maintaining producing assets in safe and operable conditions and ensuring compliance to data management by Petroleum Agreement Contractors (PACs). For mature fields nearing expiry of production sharing contracts (PSCs), it is even more critical to safeguard the integrity of petroleum facilities and to conduct an inventory check of acquired data during transition from existing to new PACs. A Due Diligence Audit (DDA) provides an important milestone to benchmark the health of existing assets (subsurface and surface) and outlining roadmap for future development opportunities for the PSC fields. This paper presents key technical results and value creation areas from the DDA conducted for one of the largest gas field PSC in Malaysia.

This gas PSC consisted of multiple gas fields and production hubs catering to a majority of gas production in the region. Although the fields had been in production for more than 20 years, maintaining production plateau rate and optimizing operating cost were identified as key concerns for long term sustainability. New development opportunities were also needed to mitigate the same. For existing fields, incremental recovery projects focused on lowering the abandonment pressure are planned. To maximize the utilization of gas processing capacity in production hubs, nearby gas fields have also been identified for cluster development and evacuation. Assurance on long-term gas supply is targeted through fast pace exploration in the early years of new PSCs to discover new gas development areas and to further increase the operating life of these hubs. As ageing assets, each of the fields also faced unique challenges such as liquid handling, subsidence issues and increasing inventory of idle wells. Through successful application of the DDA framework, a detailed technical assessment of deliverables was conducted along with liability management to address these asset integrity risks.

With the successful completion of DDA for these fields, the technical assessment deliverables have created significant PSC value by securing identified opportunities under minimum work commitments. In addition, it facilitated a roadmap for idle wells management plan and new technology in "Implement Replicate" phasing. This has helped PETRONAS to further monetize opportunities in ageing assets, and safeguard producing hubs for long-term gas supply.

This paper presents an efficient Due Diligence Audit workflow for long term value creation in mature fields and assets.
Application of Enhanced RPB Tool in Malaysian Oilfields for Gas Injection RF Benchmarking and Analogue Identification
Authors: Sinha Ankaj Kumar , Jalan Shlok , Ranjan Rakesh , Masoudi Rahim
Published: October 2021

Identification of an optimal field development plan is one of the most critical decisions for oil asset management. In the new norm of low oil prices, this assumes even more relevance for mature oilfields to maximize overall recovery. In greenfield developments and oilfields in early production life, the absence of a clear roadmap detailing their future development strategy can often lead to missed opportunities and sub-optimal recovery. To bridge this gap, Petroliam Nasional Berhad (PETRONAS) began identifying new improved oil recovery (IOR) opportunities in mature fields and started formulating an optimal development plan in greenfields. In 2019, PETRONAS embarked on implementing the Reservoir Performance Benchmarking (RPB) tool to evaluate reservoir recovery potential with waterflood. This paper will detail the Phase-II development of this data-analytics based tool which focuses on delivering a comprehensive roadmap, which includes other recovery mechanisms such as gas injection and reservoir under primary recovery.

Phase-I of RPB tool (SPE-196443) had considered water injection as a key secondary recovery process to evaluate the benchmark recovery factor for an oil reservoir. As part of Phase-II development, this has been further enhanced to evaluate field recovery potential and provide the benchmark recovery factor for primary recovery and gas injection processes. For fields under primary recovery, a comparative assessment between volumetric depletion and varying aquifer/gas-cap drive is conducted to ascertain the recovery potential. Assessment for incremental secondary recovery gains considers both gas injection and water injection scenarios in the enhanced benchmarking methodology. In addition, the benchmarking calibration methodology has also focused on incorporating additional reservoir parameters specific to each of the recovery processes for overall estimation of the benchmark value. The benchmarking tool also identifies analogue reservoirs to enable replication of best development practices and optimization of the development strategy.

With the deployment of this enhanced RPB tool, a comprehensive 5-year roadmap has been developed to improve Malaysian oilfields recovery. This has enabled PETRONAS to augment its resource funnel inventory, streamline its opportunity ranking and integrate project maturation tracking with existing digital platforms of its entire portfolio.

This is a novel benchmarking tool to assess reservoir potential recovery factor for primary and secondary recovery processes (both water and gas injection) along with analogue identification for replication of best development practices