Gowida, Ahmed (King Fahd University of Petroleum & Minerals) | Elkatatny, Salaheldin (King Fahd University of Petroleum & Minerals) | Abdulraheem, Abdulazeez (King Fahd University of Petroleum & Minerals)
Formation density plays a central role to identify the types of downhole formations. It is measured in the field using density logging tool either via logging while drilling (LWD) or more commonly by wireline logging, after the formations have been drilled, because of operational limitations during the drilling process that prevent the immediate acquisition of formation density.
The objective of this study is to develop a predictive tool for estimating the formation bulk density (RHOB) while drilling using artificial neural networks (ANN). The ANN model uses the drilling mechanical parameters as inputs and petrophysical well-log data for RHOB as outputs. These drilling mechanical parameters including the rate of penetration (ROP), weight on bit (WOB), torque (T), standpipe pressure (SPP) and rotating speed (RPM), are measured in real time during drilling operation and significantly affected by the formation types. A dataset of 2,400 data points obtained from horizontal wells was used for training the ANN model. The obtained dataset has been divided into a 70:30 ratio for training and testing the model, respectively.
The results showed a high match with a correlation coefficient (R) between the predicted and the measured RHOB of 0.95 and an average absolute percentage error (AAPE) of 0.71%. These results demonstrated the ability of the developed ANN model to predict RHOB while drilling based on the drilling mechanical parameters using an accurate and low-cost tool. The black-box mode of the developed ANN model was converted into white-box mode by extracting a new ANN-based correlation to calculate RHOB directly without the need to run the ANN model. The new model can help geologists to identify the formations while drilling. Also, by tracking the RHOB trends obtained from the model it helps drilling engineers avoid many interrupting problems by detecting hazardous formations, such as overpressured zones, and identifying the well path, especially while drilling horizontal sections. In addition, the continuous profile of RHOB obtained from the developed ANN model can be used as a reference to solve the problem of missing and false logging data.
With the recent tremendous development in algorithms, computations power and availability of the enormous amount of data, the implementation of machine learning approach has spurred the interest in oil and gas industry and brings the data science and analytics into the forefront of our future energy. The idea of using automated algorithms to determine the rock facies is not new. However, the recent advancement in machine learning methods encourages to further research and revisit the supervised classification tasks, discuss the methodological limits and further improve machine learning approach and classification algorithms in rock facies classification from well-logging measurements. This paper demonstrates training different machine learning algorithms to classify and predict the geological facies using well logs data. Previous and recent research was done using supervised learning to predict the geological facies.
This paper compares the results from the supervised learning algorithms, unsupervised learning algorithms as well as a neural network machine learning algorithm. We further propose an integrated approach to dataset processing and feature selection. The well logs data used in this paper are for wells in the Anadarko Basin, Kansas. The dataset is divided into training, testing and evaluating wells used for testing the model. The objective is to evaluate the algorithms and limitations of each algorithm. We speculate that a simple supervised learning algorithm can yield score higher than neural network algorithm depending on the model parameter selected. Analysis for the parameter selection was done for all the models, and the optimum parameter was used for the corresponding classifier.
Our proposed neural network algorithm results score slightly higher than the supervised learning classifiers when evaluated with the cross-validation test data. It is concluded that it is important to calculate the accuracy within the adjacent layers as there are no definite boundaries between the layers. Our results indicate that calculating the accuracy of prediction with taking account the adjacent layers, yield higher accuracy than calculating accuracy within each point. The proposed feed-forward neural network classifier trains using backpropagation (gradient descent) provides accuracy within adjacent layers of 88%. Our integrated approach of data processing along with the neural network classifier provides more satisfactory results for the classification and prediction problem. Our finding indicates that utilizing simple supervised learning with an optimum model parameter yield comparable scores as a complex neural network classifier.
This paper has an objective of identifying the nature of formation fluid from an extreme tight fractured reservoir. A good understanding of petrophysical properties of the reservoir rock as well as the fluid it contains constitutes a real challenge for tight reservoirs, that are the most common unconventional sources of hydrocarbons. The front-line characterization mean is the Wireline logging which comes directly after drilling the well or while drilling, knowing that for low to extreme low porosity-permeability reservoirs any attempt of conventional well testing will not bring any added value not rather than a confirmation of reservoir tightness. A tailored workflow was adopted to design the most appropriate formation testing module, select the best depths for fluid sampling, and distinguish hydrocarbon from water bearing intervals. This workflow involves ultrasonic and Electric Borehole Images in combination with Sonic Scanner for natural fractures detection, localization and characterization, integrating Dielectric recording and processing for petrophysical evaluation, then Formation Testing was carried out for fluid identification and sampling. The use of borehole electric and sonic imager coupled with advanced sonic acquisition helped not only to identify the natural fractures depths, but also the nature of these fractures. This integration was used for selecting the sampling station.
This seminar will teach participants how to identify, evaluate, and quantify risk and uncertainty in everyday oil and gas economic situations. It reviews the development of pragmatic tools, methods, and understandings for professionals that are applicable to companies of all sizes. The seminar also briefly reviews statistics, the relationship between risk and return, and hedging and future markets. Strategic thinking and planning are key elements in an organisation’s journey to maximise value to shareholders, customers, and employees. Through this workshop, attendees will go through the different processes involved in strategic planning including the elements of organisational SWOT, business scenario and options development, elaboration of strategic options and communication to stakeholders.
Learn more about training courses being offered. Learn more about training courses being offered. This course covers the fundamental principles concerning how hydraulic fracturing treatments can be used to stimulate oil and gas wells. It includes discussions on how to select wells for stimulation, what controls fracture propagation, fracture width, etc., how to develop data sets, and how to calculate fracture dimensions. The course also covers information concerning fracturing fluids, propping agents, and how to design and pump successful fracturing treatments. Learn more about training courses being offered. Current and future SPE Section and Student Chapter leaders are invited to engage and share. Every attendee leaves energised with a full list of ideas and a support network of fellow leaders. Those sections and student chapters actively participating in this workshop have consistently been recognized with awards as the best in SPE. SPE Cares is a global volunteering drive aimed at promoting, supporting and participating in community services at the SPE section and student chapter’s level. On its official launch this year at ATCE Dubai, SPE Cares will conduct a “Give a Ghaf” Tree Planting Programme to help preserve Ghaf’s cultural and ecological heritage. The Ghaf tree is an indigenous species, specific to UAE, Oman and Saudi Arabia. It is a drought tolerant, evergreen tree that can survive a harsh desert environment. The initiative not only aims to hold events/activities at ATCE, but also recognise community service that SPE members are already conducting in their respective student chapters and professional sections. The KEY Club, open daily, is an exclusive lounge for key SPE members. The lounge is open to those with 25 years or more of continuous membership, Century Club members, current and former SPE Board officers and directors, Honorary and Distinguished Members, as well as this year’s SPE International Award Winners and Distinguished Lecturers. DSATS (SPE’s Drilling Systems Automation Technical Section) will hold a half-day symposium featuring keynote presentations on urban automation. This symposium will explore technologies being used in developing smart cities through the automation of their infrastructure, transportation systems, energy distribution, water systems, street lighting, refuse collection, etc. These efforts rely on many of the same tools needed for drilling systems automation yielding increased efficiencies, lower maintenance and reduced emissions. Their knowledge and experience can guide the path being travelled by the oilfield drilling industry.
Saudi Aramco awarded a contract* to McDermott International for engineering, procurement, constructi ... DNV GL has won a contract to provide independent verification services for Energinet’s section of th ... Equinor has awarded Subsea 7 with an engineering, procurement, construction, and installation (EPCI) ... Malaysian FPSO provider Yinson has secured a long-term contract extension through October 2022 with ... Well-Safe Solutions has been awarded a contract to decommission as many as 21 wells on the DNO-opera ... Infinity Oilfield Services and Medserv have formed a new joint venture named InMedCo to provide a po ... Boskalis Subsea Services said it has been awarded more than £100 million ($126 million) in contracts ... Chevron Australia has awarded Wood with a new contract to provide subsea integration and flow assura ... Qatar Petroleum has awarded McDermott with the FEED contract for the offshore pipelines and topsides ... DOF Subsea has secured two contracts in Brazil. Source: Teekay Offshore Teekay Offshore has agreed to a 3-year contract ... Petrobras has awarded TechnipFMC with an engineering, procurement, construction, and installation (E ... ADNOC awarded a $1.36-billion dredging, land reclamation, and marine construction contract to the UA ... Wood and KBR inked a multimillion-dollar contract to deliver integrated front-end engineering design ... Anadarko Mozambique Area 1, LTDA, a subsidiary of Anadarko Petroleum Corp., named the preferred tend ...
Africa (Sub-Sahara) Algeria awarded four of 31 oil and gas field blocks on offer to foreign consortiums in its first auction since 2011. Shell and Repsol won permits for the Boughezoul area in the north of the country, while Shell and Statoil won permits for the Timissit area in the east. A consortium of Enel and Dragon Oil was awarded permits for both the Tinrhert and the Msari Akabli areas. Circle Oil's CGD-12 well, located onshore Morocco in the Sebou permit, encountered natural gas at different levels within the Guebbas and Hoot sands. Wireline logging analysis confirmed a net 9.7 m of pay. The first test, over the Intra Hoot sands, flowed gas at a sustained rate of 2.21 MMscf/D through an 18/64‑in. The primary target, the Main Hoot sands, flowed at a sustained rate of 4.62 MMscf/D through a 24/64-in.
Africa (Sub-Sahara) ExxonMobil subsidiary Esso Exploration Angola has started oil production at the Kizomba Satellites Phase 2 project offshore Angola. The project involves the development of subsea infrastructure for the Kakocha, Bavuca, and Mondo South fields. Mondo South is the first field to begin production, and the other two satellite fields will follow later this year. The goal is to increase Block 15's production to 350,000 BOPD. Esso (40%) is the operator with BP Exploration Angola (26.67%), Kosmos Energy discovered gas at the Tortue West prospect in Block C-8 offshore Mauritania.
Decisions in E&P ventures are affected by Bias, Blindness, and Illusions (BBI) which permeate our analyses, interpretations and decisions. This one-day course examines the influence of these cognitive pitfalls and presents techniques that can be used to mitigate their impact. Bias refers to errors in thinking whereby interpretations and judgments are drawn in an illogical fashion. Blindness is the condition where we fail to see an unexpected event in plain sight. Illusions refer to misleading beliefs based on a false impression of reality.