From digital oil˜eld to operational excellence398 From digital oil˜eld to operational excellence...

394

Copyright ©　2019， JAPT

石油技術協会誌　第 84巻　第 6号（令和元年 11月）394～ 402頁Journal of the Japanese Association for Petroleum Technology

Vol. 84, No. 6（Nov., 2019）pp. 394～402

講　　　演Lecture

1.　IntroductionIn this paper we will showcase how operators can benefit

from the drive toward digital in concrete ways that optimize the return on OPEX:

1. Smarter workovers – Automatic recognition of well and equipment under-performance and best remediation such as per forating, coiled tubing, slickline and production logging;

2. Automated logistics planning – Digitally powered automated planning （and rapid re-planning） of well and field activity logistics;

3. Equipment integri ty – Automatic detect ion of pr ogr ess ive cav i ty pump （PCP） and e lec t r ic submersible pump （ESP） equipment operational anomalies to minimize deferment and extend pump run life;

4. Automation – Har ness subsur face and sur face

automation to increase ef ficiency and production, reduce risk and improve safety.

2.　Smarter workovers

Well interventions and maintenance operations are common field activities that require significant OPEX expenditure. Schlumberger has carried out an ambitious program to digitally empower each of the key steps of asset remediation, maintenance and workover:

1. Sur veillance to detect under-per forming wells and equipment and diagnostics to determine root-cause across the production system, which may include acquiring additional measurement data;

2. Simulation of alternative workover scenarios, for example, acid placement, plugging or patching, changeout of well equipment, etc.;

3. Detailed job design and operational logistical planning followed by economic evaluation to define a prioritized queue of jobs to be performed;

4. On-the-fly job re-planning to optimize remediation activity during job execution, and mobile acquisition of data as jobs are proceeding;

5. Analytics and machine learning from post-job evaluation and look-backs to drive continuous improvement of future interventions.

From digital oil�eld to operational excellence＊

Marwa Hassan＊＊，†, David Rossi＊＊and Galina Ivanova＊＊

（Received October 7, 2019；accepted October 30, 2019）

Abstract：　Cross-cutting digital transformation driven by domain expertise and experience is re-shaping the future of the industry, giving birth to high-stake digital opportunities for all stakeholders: the creation of new work�ows and collaboration at every stage of E&P operations, impacting operational efficiency, increasing asset reliability, boosting throughput, recovery and raising safety levels across the value chain. The 4th industrial revolution is underway, fed by breakthroughs in automation, robotics, arti�cial intelligence and the internet of things. Like in many other industries, this 4th industrial revolution creates fundamental shifts in oil & gas. Of all stages of the E&P lifecycle, production operations stand to make the largest gains from this digital disruption, with an estimated 6% to 35% value at stake, where business value refers to savings or gains, such as time and cost savings through ef�cient processes or replacement of people by machines through creative destruction also known as the act of old jobs being replaced by newer ones. Driven by the promise of ubiquitous digital transformation, Schlumberger envisioned a few years back, and refocused its R&D effort since, on the delivery of such breakthrough capabilities. We strongly believe that this new approach is set to unlock the enormous potential of a digitally enabled, integrated and automated operating environment that bene�ts from the convergence of digital enablers, high-end E&P science and domain expertise.

Keywords： digital oil �eld, smarter workovers, automated logistics planning, equipment integrity, automation

＊令和元年 6月 13日，令和元年度石油技術協会春季講演会開発・生産部門シンポジウム「油ガス田開発を新世代へ～進化するデジタル技術と開発計画の再構築～」で講演　This paper was presented at the 2019 JAPT Development and Production Technology Symposium entitled “Explore the next stage ~Apply digital transformation and re-visit E&P projects~” held in Tokyo, Japan, June 13, 2019.

＊＊シュルンベルジェ　Schlumberger † Corresponding author：E-Mail：[email protected]

Marwa Hassan, David Rossi and Galina Ivanova 395

J. Japanese Assoc. Petrol. Technol. Vol. 84, No. 6（2019）

2.1 Evergreen opportunity identificationTo sustain asset production, asset teams continuously

propose target-based production enhancement opportunities subject to budget and operational constraints. Historically, identification of potential workover candidates has been a manual, tedious and repetitive process that consumes weeks of engineers’time for every iteration. Moreover, best practices and lessons learned from past interventions are rarely captured, preventing a systematic improvement of the candidate selection methodology and choice of intervention.

As par t of the transformation to intelligent fields, Schlumberger has developed a digitally-driven comprehensive reasoning system that is standardized, domain-driven and fully automated, designed for the rapid screening and ranking of thousands of well performance improvement opportunities. This power ful proactive well por tfol io optimization （WPO） backbone maintains a full and prioritized pipeline

of production enhancement opportunities and expedites workover candidates through the opportunity maturation process （Fig. 1）.

Decision-support algorithms leverage decades of proven petroleum-engineering analysis methods and best practices, combined with operator business logic, to automatically and autonomously:・ Ident i fy underper for ming wel ls through their

performance signatures, ・ Determine root causes, ・ R e c o m m e n d t h e m o s t a p p r o p r i a t e r e m e d i a l

interventions,・ Probabilistically quantify post-workover production,・ Evaluate risked economic viability of workover

candidates, ・ Estimate the chance of success for each intervention

using a continuously-enriched look-back knowledge

base.This comprehensive reasoning system generates an

evergreen list of opportunities and has recently brought significant business value in an operational oil and gas setting （Fig. 2）.

Schlumberger anticipates the high-volume nature of Project Oil Kenya and associated production management challenges to present a unique opportunity to rely upon such an autonomous opportunity management system. An upfront investment in adequate system configuration and training from Schlumberger experts, and in streamlining related data acquisition and access, could over time dramatically impact project performance and returns on OPEX for any operator. It is, for example, foreseen that a fraction of intervention jobs originally planned through conventional reactive procedures will , through such a digitally-amplified optimization environment, result in equal or higher production gains. Beyond the production enhancement impact, the ripple effect on economics and logistics resulting from an optimized job sequencing, crew deployment, products and chemicals consumption, and produced water management are expected to be significant. WPO is designed to automatically expose the full economic trade-offs of workover opportunities.

Fig. 1　Opportunity maturation process

Fig. 2 Well portfolio optimization value generation; unnamed operator（March 2019）

From digital oil�eld to operational excellence396

石油技術協会誌　84巻 6号（2019）

WPO （Fig. 3） is the result of a mult i -year f ie ld -tested Schlumberger Research & Engineering program, commercialized through DELFI, the Schlumberger Cognitive E&P Environment.

2.2 Rapid screeningThe DELFI environment of fers an intelligent, single,

shared, living ecosystem that spans the entire E&P lifecycle. Rapid screening illustrates this new way of working by redefining traditional subsurface and production workflows to get insight and interpretation faster and more consistent. Analytical interpretation takes logs, performs quality and consistency checks, analytically corrects and reconstruct logs delivering every user a quality set of logs ready for interpretations saving hours. Machine learning is central piece of subsurface interpretation resulting in fast and consistent

interpretation delivering a rapid subsurface interpretation, freeing up the exper t to focus on the complexity of the subsurface. Business intelligence tools are in key places in the workflows, for fast insight to understand the key performance drivers of the reservoir. Fig. 4 provides a snapshot of the DELFI rapid screening built-in capabilities.

2.3 Autonomous production advisoriesComplementing episodic workover and remediation

activities just described, the day-to-day management of producing fields demands a significant and steady OPEX commitment to be able to process the large amounts of data from production and equipment （along the entire hydrocarbon pathway from reser voir to the processing facilities） and maintain field production.

Since the early 2000s, digital oilfield （DOF） solutions have been routinely available that present data in dashboards along with surveillance alerts. In the 2000s, these DOF solutions brought about significant increases in the ef ficiency with which operating assets detected operational problems in the field. After an operational problem is detected in the field, a key remaining challenge not addressed by previous solution generations was the automatic translation of signals into the most appropriate corrective action to most effectively, economically, logistically and safely address the underlying issue.

Schlumberger has under taken an ambitious program to develop a digital production advisor y system that automatically detects operational issues, diagnoses root-cause and delivers situational awareness via insights and recommendations of the most appropriate corrective course of action. This advisory system leverages Schlumberger’s 15+ years of global DOF experience implementing operational solutions in dozens of geographical locations and operational settings.

This digital production advisory system （Fig. 5） allows asset teams to concentrate effort on higher value adding tasks and directly address production operational challenges in a

Fig. 3　DELFI well portfolio optimization

Fig. 4 High volume data contextualization – Automatic sweet spot identification & machine learning based log interpretation



streamlined and effective manner:・ Autonomous and actionable insights: the automated

advisory system transforms streaming field information into insights and recommended actions;

・Operational planning and optimization: activities are tracked from inception, through escalation, to completion, providing a clear trail for audit and historic lookbacks, ultimately empowering operational excellence;

・ Tracking activities: work is tracked from inception to completion, with all relevant data automatically saved in the background;

・ Evergreen opportunity identification, described in the previous section, proactively manages the opportunity pipeline, utilizing domain best practices and advanced data-driven algorithms;

・ Closing the feedback loop: machine learning from previous data, subject matter expertise and historical decisions; driving an ever-improving set of future

proactive recommendations.This approach combines petroleum engineering domain

science and digital technologies in a way that leads to a paradigm shift in surveillance and production management for wells, networks and facilities. The operational environment provides engineers with continuous and actionable insights based on a combination of data-driven techniques, first-principle models and historical look-back, all accompanied by indicators of confidence level. Based on Schlumberger’s experience delivering DOF projects globally for oil and gas operators, typical business value gain associated with DOF projects is on the order of 3 – 15% with minimal capital investment （Jha et al., 2009; Al-Khaldi et al., 2009; Lee et al., 2010）.

Essential to managing production operations is knowledge of multi-phase production flow rate. Schlumberger has industry leading digital methods to integrate direct （physical metering, single-phase and multi-phase metering） and indirect （virtual flow metering – VFM） information related to flow rate. These methods integrate all sources of information

Fig. 5　Digital production advisory system

Fig. 6 DELFI ProdOps: Exception based surveillance - automatic insight generation

Fig. 7 DELFI ProdOps: Autonomous recommended action – activity tracking



with uncertainty to provide high-quality flow rate estimates between separator well tests and provides back-up knowledge of flow rates in the event of equipment failure. Transient VFM based on measurement of mechanical pumping systems supports transient analysis for interpretation of trends in wellbore skin and average reservoir pressure （Camilleri and Zhou 2011; Camilleri et al., 2015; Camilleri et al., 2016a; Camilleri et al., 2016b）.

Digital disruption is enabling the industry to harness new forms of cognition techniques, fueled by the emergence of machine learning, artificial intelligence, and natural language processing. The next generation production operations environment Schlumberger has engineered is the result of extensive convergence of domain-based science and such emerging data-driven science. Essential to enabling a step-change in operational excellence is continuous learning.

The DELFI cognitive E&P environment is built on a data ecosystem that allows all DELFI-native applications to leverage underlying transversal intelligence. An example of such advances is the automatic delivery of documents based on configurable degrees of salience and relevance. Fig. 8 shows such data delivered in context. Such innovation stands to dramatically reduce the turn-around time to accessing relevant digitalized knowledge and experience. Whether field-based or office-based, engineers and operators have at their finger tips the ability to access to key standard work instructions, safety procedures, and analogous intervention experiences or any other related digital entities automatically detected in the connected data ecosystem.

3.　Automated logistics planning

Oil and gas operators routinely perform integrated activity planning （IAP）, to develop a master logistics plan that insures the proper flow of people and resources （e.g., materials, equipment, chemicals, vehicles） to meet a business goal, subject to constraints and adherence to standard operating procedures （SOPs） and business rules.

Planning integrates activities across multiple functions to define a sequence of operations that provide business value （e.g., minimizes cost, reduces safety risks, maximizes uptime）. Planning algorithms make use of constraints coming from strategic-level asset optimization （e.g., simulators linked to stochastic local search, linear programming, mixed-integer programming）, and combines these with knowledge in the form of rules and SOPs, to define the digital operational plan.

Activity planning outputs a task-sequenced plan – a series of decisions versus time. Each decision in a plan corresponds

Fig. 9　Subsurface-driven operational planning optimization with DELFI

Fig. 8 DELFI ProdOps: Digital ized knowledge automated delivery in context



to an action that changes the system from one state to another. Normally, activity planning is too difficult a task for a human to perform because there are far too many choices and alternative actions at each decision step for a human to comprehend. Fortunately, automated planners exist. After some up-front definitions, automated planners are capable of rapidly searching a very large solution space to identify a set of continually improving feasible planning solutions.

Planning domain definition language （PDDL） technology has been successfully applied internally by Schlumberger to institutionalize repeatable and standardized methods for optimizing planning of logistics operations – insuring the proper flow of people and resources, with clear understanding of supply chain critical paths and operational constraints and disruptions. PDDL is a dynamic method – it allows rapid re-planning and activity reconfiguration mid-job, for example, in the event of an unexpected operational disruption. Applications include drilling operations, wireline logging operations, coiled tubing operations and sand delivery for unconventional fracking operations.

Optimal job re-planning; on-the-fly re-planning of wellsite jobs is a special case of automated logistics planning, and involves not only re-planning logistics （people, materials, equipment, vehicles）, but also technical modeling and re-design of the acid treatment job. One example where this has been successfully applied is when distributed fiber optics along a coiled tubing unit is used during acid stimulation. Thermal analysis of flowback, evaluated through remote operational support, allows the zonal ef fectiveness of the stimulation to be confirmed. If insuf ficient, an additional stage of acid stimulation can be planned, and the additional stage of acid placed while the coiled tubing unit is still on the well, maximizing operational efficiency （Ahmed et al., 2013; Ahmed et al., 2014; Buhassan et al., 2015）.

The automated approach to reser voir and production

management opportunity identification previously described sits at the heart of optimized intervention job scheduling. Fur ther integration of business constraints （logistical, f inancial , HSE） traditionally managed in enterprise resources planning （ERP） / business systems allows for a comprehensive approach to asset management. Schlumberger foresees such a system to greatly impact the performance of Project Oil Kenya in that an evergreen optimized well intervention job sequence and well servicing queue is ensured at all stages of operations （Fig. 9）.

4.　Equipment integrity

Fast-loop pump surveillance has been proven to provide tactical and strategic business benefits, including minimizing lost production, optimizing the timing of intervention activity, providing key well and reservoir properties like skin and average reservoir pressure and extending the useful life of equipment. Schlumberger has developed ar tificial lift surveillance capabilities that allow remotely situated pumps to be monitored in real-time by domain experts in a surveillance center, as well as probabilistic and data analytic methods that automatically monitor multiple channels of ESP data to detect operational anomalies through pattern recognition （Fig. 10 left）. High-frequency （minute to hour） monitoring quickly identifies and diagnoses operational issues. Event detection then triggers quick remediation of pump issues, and this has been demonstrated to improve pump run life （Fig. 10 right）.

In the context of wellbore pumps and subsea equipment, recent work has led to algorithms for prognostic health monitoring （PHM）. These algorithms detect emerging mechanical issues through trend analysis of key parameters such as vibration. Predictive recognition of impending failures triggers a maintenance management workflow and associated logistics planning to prepare for equipment replacement workover activity prior to failure （Fig. 11）.

Fig. 10 Left: ESP low flow event （Blanckaert et al., 2013）; Right: Fast-loop event detection and remediation leads to improved ESP survivability （Camilleri and Macdonald, 2010; Camilleri et al., 2010）



5.　Automation

As the oil and gas industry harnesses digital components to provide powerful solutions, a key element is automation. Automation improves business performance by improving efficiency and requiring fewer people in the field （domain exper ts remain in centralized sur veillance and control centers）, improving safety, security and environmental per formance （intrusion and leak detection, corrosion detection, early warning of toxic gas）, and improving logistics （mobility and tracking capabilities）.

Closed loop automation; as digital and machine learning algorithms （e.g., automated event detection） develop and mature, there has been a trend for these algorithms to move close to field equipment locations. This is particularly true for closed-loop field automation solutions combining subsurface and wellsite surface equipment.

The distribution of computation toward field equipment means that data from field sensors, and control signals back to field actuators, do not have to travel to and from a remote centralized location. This has several benefits, including:・ Reduced latency （data travel time） for more rapid

response to events;・ Ability to process very large amounts of data such as

fiber optic vibration signals （no requirement for large

bandwidth to communicate data from the field）;・ Fast, distributed optimization of the dynamic production

system.The challenge with distributed computation is that it is fully

automated and thus needs to be extremely robust so it can function without human interaction.

An area of recent progress has been connecting subsurface and sur face sensors and actuators, for example for simultaneous control of subsur face pumps and sur face chokes. There has also been significant recent progress connecting sensors and actuators along the hydrocarbon pathway in Fig. 12: from the reservoir, through the well and surface gathering system to the process facilities. Distributed real-time measurement data can be combined with modeling technologies such as integrated asset modeling （IAM）（Espinola et al., 2014） to develop solutions for automated

control and optimization of the entire integrated production system.

The increasing convergence of industries, driven by the proliferation of connected objects and ubiquitous connectivity unlocking data access, is reshaping the Oil & Gas opportunity landscape. Upfront integration of wellsite process control and automation promises to transform operational efficiency, to minimize environmental and safety risks, to optimize asset integrity, ultimately minimizing production deferment. Remote, stranded and high well volume assets stand to benefit the most from such technology convergence gains. Schlumberger has taken a leading role in enabling these new levels of operational excellence, as reflected in the 2019 announcement of the intention to form with Rockwell Automation the Sensia joint venture （JV）, a new standard in digital oilfield and automation.

The Sensia JV will be the first fully integrated provider of measurement solutions, domain expertise, and automation to the oil and gas industry. It will offer scalable, cloud and edge-enabled process automation, including information and process safety solutions. From intelligent systems to fully engineered life-cycle management automation solutions, the

Fig. 11　Prognostic health management （PHM） to detect indicators of impending failure

Fig. 12 Integrated management of reser voir, well, gathering system and facilities



joint venture will help oil and gas operators drive efficiency gains through measurement and data-driven intelligent automation. Sensia JV will provide outcomes;・ Improved visibility to disconnected data・ Elimination of manual process to drive production

efficiencies・ Asset optimization and automation・ Safety & Security （Personnel & Assets）

References

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Ahmed, D., Burov, A., Baez, F. and Srivastava, A., 2013: Innovative Abrasive Jetting Selective Stimulation Utilizing Fiber Optic Enabled Coiled Tubing Revive Oil Producer Wells with High Water Cut. SPE Middle East Oil and Gas Show and Conference, Manama, Bahrain, 10-13 March, SPE-164157-MS, doi: 10.2118/164157-MS.

Ahmed, D., Ramondenc, P. and Baez, F., 2014: Advanced Understanding of Matrix Stimulation Treatments Through In-Job and Post-Job Evaluation of Real-Time Downhole Measurements with Coiled Tubing: A Case Study. SPE International Symposium and Exhibition on Formation Damage Control, Lafayette, Louisiana, USA, 26-28 February, SPE-168156-MS, doi: 10.2118/168156-MS.

Blanckaert, T. and Subervie, Y. M., 2013: Real-Time Data

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Fig. 13　The intelligent oilfield



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デジタルオイルフィールドから

オペレーショナル・エクセレンスへ

マーワハッサン・デビッドロッシガリーナイヴァノヴァ

石油ガス業界におけるデジタルトランスフォーメーション

は，各分野の専門的な知見，経験をベースに分野横断的に

進められ始めており，将来的には業界を左右していくもの

となり得る。この流れは，石油開発における各ステージで

の新たなワークフローや協働のあり方の創出，操業効率化，

アセットの価値や信頼性の向上，スループットの押し上げ，

バリューチェーンの改善といった便益を生み出し得る。

オートメーション，ロボティクス，人工知能や IoTといった要素により第 4次産業革命が分野，業界を問わず推し進められている中，石油ガス業界にもこの流れは認知されて

いる。デジタルトランスフォーメーションによる生産操業

における便益は，操業効率化や機械による人件費削減から

もたらされる時間・コスト削減により，6％から 35％に及ぶとも推定され，石油開発におけるライフサイクルの中で

最大の価値創出ポテンシャルを持つとされている。

本稿では具体的事例をベースに，石油開発における操業

コストを最適化する方法として smarter workover，物流計画の自動化，設備・機器のインテグリティ，オートメーション

の 4分野を挙げ，デジタルトランスフォーメーションがもたらす価値について述べる。

From digital oil˜eld to operational excellence398 From digital oil˜eld to operational excellence...

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