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US Job Numbers Up for OFS and Equipment Industry, But Outlook Remains Unclear The increase in OFS and equipment sector jobs over the past 2 months came amid higher oil and gas production. But increases in COVID-19 cases are causing uncertainty about when and how much demand will rise. Texas Regulator To Place New Limits on Allowable Flaring Oil and gas producers in the state are being asked to submit data and economic analysis on why they cannot sell natural gas before they are granted permission to flare it. UAE Has Become World’s Newest Producer of Unconventional Gas The first delivery of shale gas in the UAE marks a major milestone toward its goal of reaching 1 Bcf/D by 2030. It also signals the expansion of hydraulic fracturing in the UAE’s conventional fields.
The United Arab Emirates’ (UAE) chief energy regulator has announced that the country holds a substantial volume of newly discovered unconventional resources as it approved a 5-year spending plan for the Abu Dhabi National Oil Company (ADNOC). The Supreme Petroleum Council, which also serves as ADNOC’s board of directors, placed the estimated reserves of unconventional oil within the Emirate of Abu Dhabi at 22 billion bbl, according to a government news release on 22 November. The figure would place the UAE’s tight reservoir potential on par with that of some of the biggest plays in North America. The government also said that an additional 2 billion bbl of reserves was also recently discovered, raising the UAE’s total conventional reserve estimate to 107 billion bbl. Both the conventional and unconventional estimates were independently verified by Houston-based reserves specialist Ryder Scott.
The Abu Dhabi National Oil Company (ADNOC) announced that it has completed the first phase of its large-scale multiyear predictive maintenance project, which aims to maximize asset efficiency and integrity across its upstream and downstream operations. ADNOC says its predictive maintenance platform uses artificial intelligence (AI) technologies such as machine learning and digital twins, ADNOC’s to help predict equipment stoppages, reduce unplanned equipment maintenance and downtime, and increase reliability and safety. The company said it expects use of the platform to result in maintenance savings of up to 20%. The predictive maintenance project, which was announced in November 2019, is being implemented over four phases. ADNOC’s predictive maintenance project is part of the company’s digital acceleration program, which focuses on embedding advanced digital technologies across the company’s operations.
Securing long-term energy supply for Malaysia is one of the prime responsibilities of PETRONAS; and Malaysia Petroleum Management (MPM) has been entrusted to shape the industry and enable efficient exploitation strategies and optimal development planning of Malaysian hydrocarbon assets. Production sustainability and reserve growth/addition are among the key focus area in MPM; hence, strategies and efforts are being formulated to improve the average oil field RF to more than 40%. Objective assessment of field performance, identification of recovery gaps and defining roadmap to improve field's ultimate recovery factor are critical steps to maximize the field potential ad ultimate value. This paper demonstrates the application of a hybrid workflow, comprising of data analytics-based performance benchmarking and Field Development Plan (FDP) analog assessment, to identify potential development and field management opportunities for improving economic recovery factor of an oilfield.
This novel workflow consists of three key steps. First step involves reservoir performance assessment through application of diagnostic plots, decline trends and pressure/production/injection history to validate existing reserves classified as ‘No Further Activity’ (NFA). NFA reserves along with maturity assessment of undeveloped/contingent resources will provide validated recovery factor for the field. Second step is gap analysis of validated recovery factor against benchmark RF computed through data analytics carried out in Reservoir Performance Benchmarking (RPB) tool. The third and final step focusses on monetizing the RF gap and replicating best development practices through assessment of analogue reservoirs and Field Development Plans (FDPs). Analogue development cases can be from reservoirs within same field or reservoirs with similar complexity index based on RPB tool. This step involves making various cross-plots to identify opportunities like infill drilling, secondary recovery requirement, optimal producer to injector ratio, waterflood & production optimization and operational excellence.
This workflow has been successfully applied to various oilfields (mature & greenfield) within Malaysia and results have been presented in this paper. The workflow has helped to identify numerous development opportunities to improve economic recovery factor e.g. new producer/injector wells, monetization plan for minor oil reservoirs, waterflood optimization and voidage management plans. These opportunities (subsurface/well/surface) are being matured for execution through MPM's enabling processes like Asset Value Framing (AVF), Asset Development Integrated Review (ADIR) and Asset Management Integrated Review (AMIR).
Application of recovery factor improvement workflow coupled with reservoir benchmarking results has facilitated opportunity identification in Malaysian oilfields and defined roadmap to augment nation's oil reserves base and improve the average oil field RF to more than 40%. Using this workflow, RF gap identification in existing oilfields can be completed in relatively short period of time and actionable plans can be framed for maximizing recovery factor of the respective field.
Mohmad, Nis Ilyani (Petronas Carigali Sdn. Bhd) | Mandal, Dipak (Petronas Carigali Sdn. Bhd) | Amat, Hadi (Petroliam Nasional Berhad) | Sabzabadi, Ali (Petroliam Nasional Berhad) | Masoudi, Rahim (Petroliam Nasional Berhad)
History Matching (HM) is one of the critical steps for dynamic reservoir modelling to establish a reliable predictive model. Numerous approaches have emerged over the decades to accomplish a robust history matched reservoir model ranging from the classical reservoir engineering approach to the widely accepted 3D numerical simulation approach and its variations. As geological complexity of the oil and gas field increases (multilayered reservoirs, heavily faulted) compounded with completion complexity (dual strings, commingle production), building a fully representative predictive reservoir model can be arduous to almost impossible task.
Artificial Intelligence (AI) and machine learning has advanced almost all major industries, including the petroleum industry in general and reservoir engineering. The objective of this paper is to outline a novel approach in history matching using a data-driven approach through Artificial Intelligence via Artificial Neural Network (ANN) and Data-Driven Analytics.
In this paper, a step by step methodology in building a reservoir model and history matching process using ANN will be described which includes data preparation and data QA/QC, spatiotemporal database formulation, reservoir model design, ANN architecture design, model training and history matching strategy. A case study of the implementation to Field "A" in Malaysian waters is presented where good to fair history matching quality was obtained for all production parameters. Field "A" is a 25kmx75km oil sandstone reservoirs of a highly geologically complex field (more than 200 major and minor faults, more than 30 reservoir layers) of more than 25 years of production. The challenges of history matching of this field does not only lie on its geologically complex structure and its corresponding subsurface uncertainties, but also on the production strategy of the wells that involved commingled dual strings production with several integrity issues that adds additional dimensions to the field's complexities. To date, Field "A" has no field wide history matched reservoir model using conventional numerical simulation method available due to the complexity of history matching. This long history matching woe is mitigated via the implementation of AI based reservoir model and Data Analytics. This novel approach is estimated to be more time and cost-efficient compared to the conventional method.
The comparison of this AI based reservoir model and history matching methodology with the conventional numerical reservoir model approach will be discussed. Furthermore, the advantages, limitations and areas of improvements of this AI based history matching methodology will also be highlighted.
The target audience of this paper would be to reservoir engineering practitioners and dynamic model simulators who is interested to learn the complementary or alternative approach in reservoir modelling apart from conventional numerical modelling in order to create time-efficient reservoir model and reducing the risks in their field development plans.
Tham, Su Li (PETRONAS Carigali Sdn. Bhd.) | Chiew, Kwang Chian (PETRONAS Carigali Sdn. Bhd.) | Hassani, Hamed (PETRONAS Carigali Sdn. Bhd.) | Chang, Claire Li Si (PETRONAS Carigali Sdn. Bhd.) | Huong, Chii Seen (PETRONAS Carigali Sdn. Bhd.) | Ariffin, M Hafizi (PETRONAS Carigali Sdn. Bhd.)
Production Enhancement (PE) opportunities in a 30 years-old Field D in Balingian province, offshore of Sarawak, Malaysia are dwindling. Behind casing opportunities (BCO) in relation to bypassed pay with good reservoir properties are either already perforated and produced or too costly and complex to be executed due to well issues. An in-house evaluation tool, Resolution Enhanced Modelling (REM), was developed by PETRONAS Petrophysics Department to evaluate and characterize thin beds or laminations. These Low-Resistivity-Low-Contrast (LRLC) sands are commonly bypassed as conventional logging tools cannot resolve their true parametric values and the apparent log responses across these zones appeared as shaly sand. By running REM across these intervals, the properties of the thin sands can be properly characterized, improving the net pay and economics of perforating and producing these reservoirs. In addition, a Rock Type (RT) based technique was used to evaluate some LRLC candidates in Field D.
REM was run on LRLC sections in idle wells to evaluate their potential. To derisk and test the methodology, Well A3 with relatively more promising results was chosen as the first well to be perforated. Moreover, both strings of Well A3 were idle which makes it operationally easier to carry out the perforation job. From the initial analysis, the LRLC intervals in Well A3 could contribute to additional reserves of 0.3 MMstb with start rate of 300 bopd.
The job required usage of barge assisted coiled tubing to pump cement and shut off existing high watercut zone and slickline to perforate through tubing. The actual job duration was prolonged from 30 days to 50 days due to monsoon season, driving the cost up to twice the planned amount. Post perforation, the initial oil rate was tested to be 500 bopd. After increasing the choke size, the well could flow at 800 bopd. Convinced by the success of Well A3, the same methodology was applied to Well C8 located at the north side of the field. Well C8 encountered operational difficulties such as lower than expected top of cement and perforation gun malfunction, resulting in only 54% of the proposed depth being perforated. Well C8 produced high gas, with initial well test rate of 10 bopd.
Managing a brownfield where the easy oil is mostly exhausted can be challenging. Therefore, the team has to be more creative in unlocking the remaining oil and prolonging the life of a well. By using REM, the overlooked potentials hidden in LRLC sands can be accurately estimated, making the economics to perforate them more attractive to pursue.
Bakar, Hasmizah (PETRONAS Carigali Sdn Bhd) | Faris W Hassan, W M (PETRONAS Carigali Sdn Bhd) | Kumar, Suman (PETRONAS Carigali Sdn Bhd) | Faliq Jamal, Ajmal (PETRONAS Carigali Sdn Bhd) | Magna Bela, Sunanda (PETRONAS Carigali Sdn Bhd) | Fiqri Hairi, Helmi (PETRONAS Carigali Sdn Bhd) | Latif, Nurlizawati (PETRONAS Carigali Sdn Bhd) | Hashim, Saharul (Halliburton) | Tham, Dennis (Halliburton) | Shahabuddin, Syukri (Halliburton)
A 7-in. single-trip multizone (STMZ) gravel pack system was installed successfully in two wells in the T field, Sarawak offshore. This paper highlights the system performance and knowledge obtained during this first-time installation performed in Malaysia. The most common sand control techniques established in the H, I, and J sands of this mature field include stacked gravel pack, 9 5/8-in. single-trip multizone gravel pack, and openhole standalone sand screen (OHSAS) systems. Internal gravel pack completions have provided proven, robust sand control for the sand-prone reservoirs in the T field and can save four to five days of rig time depending on the well configuration, compared with the standard stacked gravel pack completion, which was initially planned during the field development plan (FDP) stage. This paper presents the extensive technical works performed post-FDP approval to ensure the change from the 7-in. stacked gravel pack to the 7-in. single-trip multizone gravel pack completion was executed safely and efficiently and most importantly able to maximize the recoverable reserves from the multiple unconsolidated reservoirs. The technical challenges, such as unexpected drilling of additional zones, limited annulus clearance between the 7-in. liner and gravel pack tool string to reverse out proppant efficiently, intersands spacing, and gross sand interval constraints within certain tolerance because of bottomhole assembly (BHA) limitations, are also discussed. The 7-in. single-trip multizone gravel pack installation helped reduce rig time and provided a cost savings of nearly USD 1.1 million. Subsequently, the two oil-producing (OP) wells (two OP wells and four OP strings) are producing sand-free at higher than expected reserve and flow rates.
Cheng, Zhong (Xi'an Shiyou University and CNOOC Ener Tech-Drilling & Production Co.) | Xu, Rongqiang (CNOOC Ener Tech-Drilling &Production Co.) | Yu, Xiaolong (CNOOC Ener Tech-Drilling &Production Co.) | Hao, Zhouzheng (CNOOC Ener Tech-Drilling &Production Co.) | Ding, Xiangxiang (CNOOC Ener Tech-Drilling &Production Co.) | Li, Man (CNOOC Ener Tech-Drilling &Production Co.) | Li, Mingming (CNOOC Ener Tech-Drilling &Production Co.) | Li, Tiantai (Xi'an Shiyou University) | Gao, Jiaxuan (Xi'an Shiyou University)
Upstream Oil & Gas industry recognizes that there are significant gains to be had by the implementation of new digital technologies. For offshore exploration and development, the goal is to bring together all domains, all data, and all engineering requirements in a seamlessly interconnected solution. The industry is putting significant efforts into using instrumentation and software to optimize operations in all domains for exploration and production (E&P) to move towards the digital oil field of the future. an innovative digital solution has been designed and implemented to cover all different aspects of the well planning and engineering workflows, delivering a step change in terms of capabilities and efficiency.
As part of this transformation process, CNOOC have implemented integrated data management project of geological engineering for covering all different aspects of the well engineering workflows, delivering a step change in terms of capabilities and efficiency. The objective is to provide a continuous improvement platform to users for:
Digitalization can reduce the time spent with daily documentation and simultaneously increase the quality by removing an error prone way of work.
Technological solution enabling real-time data transmission from all rigs to CNOOC onshore headquarters and enabling real-time visualizations of the drilling data. This includes workload, number of needed rigs, daily performance, key performance indicators and even operation time forecasts based on real data.
Engineering solution to transform expert experience and accident cases into information to easily identify the areas of operational improvement allowing to implement specific measures to reduce intangible loss time (ILT) and non-productive time (NPT) which can help in reducing costs.
This project has also provided a real geological drilling environment where high frequency real-time drilling data is utilized along with low frequency daily drilling report data to provide better insights for well planning and generate ideas for improving performance and reducing risk.
This paper presents a full description of a new industry standard digital well construction solution that has the potential to transform the well operation process by providing a step change in collaboration, concurrent engineering, automation, and data analytics. Furthermore, the cloud-deployed solution challenges will be briefly discussed.
The learned lessons and gained experiences from this project construction presented here provide valuable guidance for future demands E&P and digital transformation.
Long recognized for producing the world’s lowest-cost crude oil, Saudi Aramco is also looking to deliver shareholder value by efficiently expanding its petrochemicals business globally, and in doing so, delivering innovative downstream projects. The company’s project teams, therefore, must routinely evaluate execution risk in the development and execution of major petrochemical projects globally. With its accountability to shareholders in mind, the company’s project leadership gathered in-depth insights into the various region-specific factors—e.g., engineering costs, equipment sourcing, contracting strategies, local construction norms—which, if left unchecked, are known to quickly erode the capital effectiveness and value of major projects. Saudi Aramco commissioned a creative two-pronged assessment—one following a bottom-up approach, the other a top-down approach—of the potential risks associated with delivering major petrochemicals projects in four regions—China, the Kingdom of Saudi Arabia, India, and the US Gulf Coast (USGC). Wood PLC and Independent Project Analysis (IPA) Inc. were tasked by Saudi Aramco’s Facilities Planning Team to conduct independent comprehensive project risk assessments for each of the four regions.