Total plans to start a digital factory to tap artificial intelligence in a bid to save hundreds of millions of dollars on exploration and production projects, according to an executive. Behavior-based safety is not a new concept nor is it new at Murphy Oil. But when Murphy launched its Safety Observation Program as a digital tool, it revitalized the way the culture of safety spread throughout the company. New funding for a chatbot technology, or smart assistant, represents the latest development in the Norwegian operator’s drive toward digitalization. Rapid advances in deep learning continue to demonstrate the significance of end-to-end training with no a priori knowledge.
Estimates derived under these definitions rely on the integrity, skill, and judgement of the evaluator and are affected by the geological complexity, stage of exploration or development, degree of depletion of the reservoirs, and amount of available data. The resource classification system is summarized in Figure 1 and the relevant definitions are given below. Elsewhere, resources have been defined as including all quantities of petroleum which are estimated to be initially-in-place; however, some users consider only the estimated recoverable portion to constitute a resource. In these definitions, the quantities estimated to be initially-in-place are defined as Total Petroleum-initially-in-place, Discovered Petroleum-initially-in-place and Undiscovered Petroleum-initiallyin- place, and the recoverable portions are defined separately as Reserves, Contingent Resources and Prospective Resources. In any event, it should be understood that reserves constitute a subset of resources, being those quantities that are discovered (i.e. in known accumulations), recoverable, commercial and remaining.
New funding for a chatbot technology, or smart assistant, represents the latest development in the Norwegian operator’s drive toward digitalization. This digital deal is helping to make augmented reality a new reality for oil and gas operations. This paper demonstrates how engineers can take advantage of their most-detailed completions and geomechanical data by identifying trends arising from past detailed treatment analyses. Dubbed the technology of the decade, AI has been the catchphrase on every futurist’s tongue. From customer support chatbots to smart assistants, AI has begun to transform numerous industry verticals.
Traditionally, petroleum exploration and development teams have utilized workflows and software which require single instance installation and cater to domain-specific needs. Design results from one domain would require incorporation into applications of other associated domains to deliver team-wide engineering. This is often time consuming, requiring multiple review meetings and extra administrative effort for the drilling engineer.
To add to the complexity, whenever iterations or sensitivity evaluations are needed across the entire plan, there is often no simple platform within which all the required processes can be managed, requiring engineering evaluations to be executed across multiple software. An example is hydraulics which is required for mud design, bottom hole assembly (BHA) and bit design, hole cleaning and borehole stability aspects of drilling. Although all these engineering considerations evaluate the same fluid properties, they typically sit on separate engines and are only integrated by criteria and thresholds in the final plan and not through concurrent engineering design.
This paper presents a new cloud deployed well construction planning solution, that aims to resolve these historical challenges by enabling multiple processes to be connected and executed from a common contextual dataset in a single system. For example, the hydraulics design is coherent across all design tasks which increases planning efficiency and plan quality. The entire solution also integrates across domains, from geology and geomechanics to drilling engineering and service company planning. This coupled with project orchestration, team collaboration and data management provide further productivity gains and cost savings for the entire team.
This paper summarizes the digital well construction planning solution and provides case study examples of how cross domain experts plan concurrently in a single common system. This approach allows a teamwide focus on planning better wells faster in a single engineering solution. Case studies show how the well planning team was able to improve cross-discipline collaboration between engineering and geoscience as well as interactions with service companies. Overall, the well planning time was reduced significantly, and the reliability of the well design was ensured through the engineering validation of each task. The integrated digital well planning solution proved to be a more cost-effective solution for well planning and ensured the high-quality delivery of drilling programs.
In this paper, we present for the first time, a classification system for naturally-occurring gas hydrate deposits existing in the permafrost and marine environment. This classification is relatively simple but highlights the salient features of a gas hydrate deposit which are important for their exploration and production such as location, porosity system, gas origin and migration path. We then show how this classification can be used to describe eight well-studied gas hydrate deposits in permafrost and marine environment. Potential implications of this classification are also discussed.
Lv, Zuobin (Tianjin Branch of CNOOC Ltd.) | Gao, Hongli (Tianjin Branch of CNOOC Ltd.) | Cheng, Qi (Tianjin Branch of CNOOC Ltd.) | Cheng, Dayong (Tianjin Branch of CNOOC Ltd.) | Meng, Zhiqiang (Tianjin Branch of CNOOC Ltd.)
JZS is an offshore metamorphic rock buried hill oilfield. Both horizontal and vertical velocities of the oil field change very fast. The interval velocity of the buried hill stratum is twice that of the overlying strata, and the top surface of the buried hill fluctuates greatly with a maximum height difference of 300m. In the complex buried hill reservoir, since the current professional seismic software can not realize variable time-depth relationship in horizontal direction, which leads to the error of the trajectory form and position of the horizontal well in time domain, therefore the well trajectory in time domain is not matched with that in depth.
In this paper, a new practical trajectories matching method for buried hill horizontal wells in time domain and in depth is presented. First of all, we carried on the research on the theoretical form of horizontal well trajectory in buried hill in time domain. The research shows that the theoretical trajectory form of a horizontal well in buried hill is consistent with trend of the buried hill top surface morphology. On the basis of theoretical research, by establishing the pseudo time-depth relationship of horizontal well based on measure depth (MD) and seismic reflection two way time (TWT), we realized the accurate characterization of the trajectory form and position of a horizontal well in buried hill in time domain: (1)For normal horizontal well with no more than 90 degrees inclination angle, we can respectively establish the pseudo time-depth relationship of the horizontal well in buried hill segment and in upper segment, and then merge both time-depth relationship data into a whole; (2)For the complex horizontal well with well segment whose inclination angle is more than 90 degrees, we need firstly split the well trajectory into normal well segment and complex segment according to inclination angle, then establish the pseudo time-depth relationship in normal and complex well segments respectively. More specifically, we can split the trajectory into normal trajectory segment with the inclination angle no more than 90 degrees and complex trajectory segment with the inclination angle more than 90 degrees, for normal segment, we can establish pseudo time-depth relationship like the normal horizontal well described earlier, for complex trajectory segment, we need creatively invert the top and bottom of the complex segment to convert inclination angle of the segment to within 90 degrees, and then establish pseudo time-depth relationship of the inverted segment.
Through this method, we can obtain the accurate trajectory form and position of the horizontal well in time domain and it provides a basis for accurate geological modeling based on 3D seismic attributes constrains. The real reservoir performance of JZS buried oilfield in Bohai Bay in China has proved that the 3D geological model based on the new time-depth relationship (MD&TWT) of the horizontal wells is closer to the actual reservoir.
Tian, Kun (CNPC Research Institute of Safety &Environment Technology) | Yan, Hong-Qiao (CNPC Research Institute of Safety &Environment Technology) | Mao, Ya-Ming (CNPC Research Institute of Safety &Environment Technology) | Wu, Shun-Cheng (CNPC Research Institute of Safety &Environment Technology)
As the adoption of the high-speed development of the information technology and continuous improvement of industrial technology, huge amount of statistical data and statistical documents are accumulated in environment-safe and environment-friendly field. The big data technology drives the attention points in the safety science field turn to the data and the exploration of the security management mode established on analysis of the data. When the big data oriented thinking and mode are used, the safety management knowledge underlines the hidden danger data in production safety is disclosed, and precision application of the safety management will be promoted.
The hidden danger data of safe production is stored in text form, and data mining and machine learning model can’t deal with these non-structured (or half-structured) information directly, so it is necessary to deal with the text data through Natural language processing (NLP), and then use the data mining method to excavate the rules information. First of all, the words in the text are recombined through technologies such as Chinese word segmentation, part-of-speech labeling, named entity recognition and the like, the word of the combined vocabulary is marked, and the named entity is further identified. Secondly, the structured problem disclosing database is constructed through three steps including keyword mapping and extraction, data cleaning and integration, data selection and transformation. What’s more, by using data mining technology such as data stream sliding window model, association analysis and change mining algorithm, this paper constructs the related hidden danger analysis method. By means of mining association rules of the current hidden danger data, releasing the related type, the existence possibility and the change pattern of hidden danger and so on. Finally, the information is visualized and analyzed.
In this paper, the data mining of 10,387 safety hidden danger samples of a refinery enterprise in 2012-2017, obtained 1091 association rules, constructed 5 kinds of associated hidden danger transformation mode: the emerging mode, attenuation mode, relation change mode, result transformation mode, new adding mode. These modes are summed up the following conclusions: (1) in view of the safety hidden danger data of petroleum and petrochemical industry, this paper analyzes the language characteristics of the domain corpus, and first forms a professional dictionary with industry characteristics; (2) using Eclat association analysis algorithm to excavate the association rules between hidden danger data, and classify the rules, which mainly focus on the pipeline, valve, safety valve and other parts; (3) after analysis of the hidden danger, the leakage of the bearing box, blind plate missing, the length of the coupling shield is too short, the discharge port setting height does not meet the requirements, the automatic valve failure, the export pressure gauge real fluctuation are six problems for the second half of 2017 new problems.
By excavating the potential value of the hidden danger data, the hidden danger checking work is further optimized and improved, and the enterprise is guided to carry out targeted hidden danger checking, the centralized optimization of services is realized, the accurate application of safety management is promoted, and the safety and environmental protection management and risk control level are improved.
Data bases of numerous oil and gas companies embrace very promising potential for more informed decision-making processes. Furthermore, there is an exponential growth in the influx of generated data from an escalating parade of systems encompassing Enterprise Resource Planning (ERP), machine instrumentation, sensory networks, and escalating mixed-media and different unlabeled data. Despite that, extracting meaningful value from zettabyte-sized datasets remains problematic given the uncontrollable wealth of data and its subsequent noise caveats. Amongst those data warehouses, are a multitude of textual information. Accordingly, Text mining has garnered worldwide interest, as it is a crucial phase in the process of knowledge discovery automatically extracting unstructured to semi-structured information. The following survey covers Text Mining methods and approaches to explain their effectiveness in information retrieval from textual databases from various sources. Moreover, the situational types where each technique may be beneficial are explored.
When Sidd Gupta's friend lost his job and struggled to find a new position after the major oil downturn in 2014, Gupta noticed a systemic problem within the industry. "A company rejected him because he was unfamiliar with the software they used in their operations," Gupta said. "In our industry, companies will judge a potential hire's technical capabilities based on which software they know how to use rather than how good they would be at the job." While software requirements for oilfield jobs are common, it made Gupta consider how we can make complex data and knowledge more accessible. Gupta saw something else brewing in the energy industry that also piqued his interest.