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Abstract On a Deep Gas Field in the Middle East, it is required to drill across a highly fractured and faulted carbonate formation. In most wells drilled across the flank of this field, it is impossible to cure the encountered losses with conventional or engineered solutions. Average time to cure losses is 20 days. With the current drive for cost optimization, it has become necessary to eliminate the NPT associated with curing the losses. A thorough risk assessment was conducted for wells drilled on the flank of this field, it was established that the risk of encountering total losses was very high. Seismic studies were performed and it was observed it would be impossible to eliminate total losses as fractures were propagated in all directions. It was proposed to run a sacrificial open hole bridge plug above the loss zone and sidetrack the well instead of performing extensive remedial operations. The proposed solution would help eliminate the well control and HSE risks associated with drilling blindly ahead with the reservoir formation exposed. Applied the proposed solution on the next well that was drilled on the flank of the field, encountered total losses, spotted eight LCM pills, unable to cure the losses, ran sacrificial open hole bridge plug and sidetracked the well. The entire process was completed in 30 hours. Sidetracked the well in adjacent direction to the initial planned well trajectory based on further seismic data analysis and no losses was encountered. Recovered full mud column to surface thus ensuring the restoration of all well barrier elements. This solution has since been adopted as best practice for wells drilled on the flank of the field where there is high probability of encountering total losses. The average time saving per well due to this optimized solution is 450 hours for wells where total losses are encountered. This engineered solution has made drilling wells on the flank of the field in a timely manner possible and at optimized costs. This has resulted in: –The elimination of Non-Productive Time, –Quick delivery of the well to production, –Reduced HSE risk, –Reduced well control risk as loss zone is quickly isolated before drilling ahead. This paper will explain why running sacrificial open hole bridge plugs and sidetracking the well is a more effective solution compared to extended remedial operations when total losses are encountered while drilling across highly fractured / faulted formation. It will discuss the extensive risk assessment conducted, the mitigation and prevention measures that were put in place in order to ensure successful implementation on trial well.
Abstract Monitoring and surveillance (M&S) is one of the key requisites for assessing the effectiveness and success of any Improved Oil Recovery (IOR) or Enhanced Oil Recovery (EOR) project. These projects can include waterflooding, gas flooding, chemical injection, or any other types. It will help understand, track, monitor and predict the injectant plume migration, flow paths, and breakthrough times. The M&S helps in quantifying the performance of the IOR/EOR project objectives. It provides a good understanding of the remaining oil saturation (ROS) and its distribution in the reservoir during and after the flood. A comprehensive and advanced monitoring and surveillance (M&S) program has to be developed for any given IOR/EOR project. The best practices of any such M&S program should include conventional, advanced and emerging novel technologies for wellbore and inter-well measurements. These include advanced time-lapse pulsed neutron, resistivity, diffusion logs, and bore-hole gravity measurements, cross-well geophysical measurements, water and gas tracers, geochemical, compositional and soil gas analyses, and 4D seismic and surface gravity measurements. The data obtained from the M&S program provide a better understanding of the reservoir dynamics and can be used to refine the reservoir simulation model and fine tune its parameters. This presentation reviews some proven best practices and draw examples from on-going projects and related novel technologies being deployed. We will then look at the new horizon for advanced M&S technologies.
Hasnan, Zurriya Hayati (PETRONAS Upstream) | Ayub, Amir (PETRONAS Upstream) | Ismail, Mohammad Hishamuddin (PETRONAS Upstream) | Harris, Mariah (PETRONAS Upstream) | Chin, Soon Mun (PETRONAS Upstream) | Syed Khastudin, Syarifah Nur (PETRONAS Upstream) | Mansor, Nur Yusra (PETRONAS Upstream) | Tengku Hassan, Tengku Mohd (PETRONAS Upstream) | Ahmad Sharif, Noor Farahida (PETRONAS Upstream) | Legrand, Xavier (PETRONAS Upstream)
Abstract OBJECTIVE / SCOPE The Black Sea is a Mesozoic-Cenozoic closed sea system representing one of the last few exploration frontiers in the vicinity of the European market. The overall prospectivity of the basin and associated regional prospective trends have been delineated using the integrated Play-Based Exploration approach. The tectonic evolution, basin formation, sedimentary infilling history, petroleum systems, and sedimentary plays have been investigated to search for new hydrocarbon potential in the basin. METHODS, PROCEDURES, PROCESS The seismic interpretation and mapping were based on 26 sparse 2D seismic lines (ION SPAN), which were acquired and processed in 2011-2012 by ION GTX. The multi-client data from offshore Russia, Crimea, and Ukraine were excluded due to geopolitical sanction. The seismic interpretation which was completed in the depth domain (PSDM depth) was calibrated using three Deep Sea Drilling Project (DSDP) wells namely Sites 379, 380, and 381 (Fig. 1) which penetrated only the shallower section namely the Top Miocene and Top Pliocene. However, the seismic markers where lacking well penetration were primarily interpreted based on seismic stratigraphy. Interpretation of the acoustic basement as well as crustal types were supplemented with gravity and magnetic data from Getech Globe’s database. Three key seismic lines (Fig. 1) were then selected to illustrate the overall basin geomorphology, structural evolution, and to subsequently identify play potential within the basins. The structural analysis was integrated with the seismic sequence stratigraphic analysis to understand the sedimentation history, depositional trends, kinematic evolution, and tectonic history.
Abstract Complete mud loss is a severe costly drilling problem which increases drilling time and makes wells challenging to control. Such situation was encountered during drilling of three wells from a platform in an offshore field in Persian Gulf. This unexpected problem occurred while other wells from this platform were drilled and completed according to the routine plan. Further investigations using 3D seismic data showed that, these wells had penetrated through a massive collapse feature causing extended drilling times. Buried collapse features are developed as result of karstification due to dissolution of carbonate rocks. Although karstification may be evaluated as a disturbing parameter of reservoir properties, the most critical concern about this phenomenon is difficulties in forecasting drilling mud weight due to highly unpredictable fluid transmissibility. Collapse feature has different viscoelastic properties from its surrounding sediments, therefore, it generates different seismic responses in terms of amplitude and frequency. This aspect was utilized for identification of encountered collapse feature where, a practical semi-automated approach based on seismic derived multi-attributes cubes and neural network analysis was taken. Once a sinkhole cube was generated based on the above approach, geobody of the collapse feature was extracted. Results then were thoroughly validated by mud loss intervals at wells. Close investigation of the sinkhole cube revealed that seven collapse features exist in the area of study in which, problematic wells pass through one of them. It was discovered that all collapse features are interconnected with deep-seated faults which acted as hydrocarbon migration conduit. This incident obviously emphasizes role of geohazards analysis before any offshore drilling, even in a developed field with several number of wells. As experienced, a significant part of well non-productive time can be avoided if an integrated multi-disciplinary approach is taken. The authors will describe an effective proactive approach towards geohazards and present lessons learned in the case presented to address unexpected geo-drilling incidents.
Real-time analysis of microseismic events using data gathered during hydraulic fracturing can give engineers critical feedback on whether a particular fracturing job has achieved its goal of increasing porosity and permeability and boosting stimulated reservoir volume (SRV). Currently, no perfect way exists to understand clearly if a fracturing operation has had the intended effect. Engineers collect data, but the methods used to gather it, manually sort it, and analyze it provide an inconclusive picture of what really is happening underground. Daniel Stephen Wamriew, a PhD candidate at the Skolkovo Institute of Science and Technology (Skoltech) in Moscow, said he believes this can change with advances in artificial intelligence and machine learning that can enhance accuracy in determining the location of a microseismic event while obtaining stable source mechanism solutions, all in real time. Wamriew presented his research at the 2020 SPE Russian Petroleum Technology Conference in Moscow in October in paper SPE 201925, “Deep Neural Network for Real-Time Location and Moment Tensor Inversion of Borehole Microseismic Events Induced by Hydraulic Fracturing.” The paper’s coauthors included Marwan Charara, Aramco Research Center, and Evgenii Maltsev, Skolkovo Institute of Science and Technology. Skoltech is a private institute established in 2011 as part of a multiyear partnership with the Massachusetts Institute of Technology. “People in the field mainly want to know if they created more fractures and if the fractures are connected,” Wamriew explained in a recent interview with JPT. “So, we need to know where exactly the fractures are, and we need to know the orientation (the source mechanism).” It Starts With Data “Usually, when you do hydraulic fracturing, a lot of data comes in,” Wamriew said. “It is not easy to analyze this data manually because you have to choose what part of the data you deal with, and, in doing that, you might leave out some necessary data that the human eye has missed.” To solve this problem, Wamriew proposes feeding microseismic data gathered during a fracturing job into a convolutional neural network (CNN) that he is constructing (Fig. 1). Humans discard nothing. Wave signals from actual events along with noise of all kinds goes into a machine, and the CNN delivers valuable information to reservoir engineers who want to understand the likely SRV. Companies today can identify the location of microseismic events, even without the help of artificial intelligence—though the techniques are always open to refinement—but analyzing the orientation (and hence their understanding of whether and how the fractures are connected) is a difficult and often expensive task that is usually left undone. “Current source mechanism solutions are largely inconsistent,” Wamriew said. “One scientist collects data and performs the moment tensor inversion, and another does the same and gets different results, even if they both use the same algorithm. When we handle data manually, we choose the process, and, in doing so, we introduce errors at every step because we are truncating, rounding up, and rounding down. We end up with something far from reality.”
Summary Hydrocarbon fields that are located offshore Abu Dhabi, United Arab Emirates (UAE), are known to be associated with undulating thick sedimentary sequences. These undulations are mostly influenced by variations in the depth of Infracambrian Hormuz salts that generate negative gravity anomalies. Nonetheless, a few known oil fields are uncorrelated with the airborne gravity observations. This is attributed to the interference from large positive gravity anomalies from basement highs. To filter out the effect of basement, we calculate the pseudogravity effect of the airborne magnetic anomalies and subtract it from the gravity anomalies. The resultant gravity anomalies mainly represent the effect of the salt domes. The results uncover deep salt structures and introduce potential traps for hydrocarbons that have proved difficult to map accurately with current seismic techniques. A nonlinear 3D inversion modeling of corrected magnetic and decreased gravity data is also used to determine the depth to basement and the Infracambrian Hormuz salts over two regions. Our findings demonstrate that the depth to basement in these regions changes from 7100 to 9700 m, and the depth to Infracambrian Hormuz salt changes from 5800 to 9400 m, with a variable thickness with a maximum of 2700 m.
It was gratifying to see that, despite the challenges inflicted by the pandemic this past year, technologies in the seismic world continued to advance. As an example, the machine-learning theme again received considerable focus. As I reviewed all the SPE seismic papers this time, the most noticeable thing was the diversity of themes and case histories that were covered. Thus, in addition to selecting three papers to be synopsized this year, I expanded the list of recommended readings to showcase that diversity. For instance, gravity and electromagnetics (and their integration with seismic) are also featured.
It was gratifying to see that, despite the challenges inflicted by the pandemic this past year, technologies in the seismic world continued to advance. As an example, the machine-learning theme again received considerable focus. As I reviewed all the SPE seismic papers this time, the most noticeable thing was the diversity of themes and case histories that were covered. Thus, in addition to selecting three papers to be synopsized this year, I expanded the list of recommended readings to showcase that diversity. For instance, gravity and electromagnetics (and their integration with seismic) are also featured. I hope you will find this broad range of topics useful.
This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 200577, “Applications of Artificial Neural Networks for Seismic Facies Classification: A Case Study From the Mid-Cretaceous Reservoir in a Supergiant Oil Field,” by Ali Al-Ali, Karl Stephen, SPE, and Asghar Shams, Heriot-Watt University, prepared for the 2020 SPE Europec featured at the 82nd EAGE Conference and Exhibition, originally scheduled to be held in Amsterdam, 1-3 December. The paper has not been peer reviewed. Facies classification using data from sources such as wells and outcrops cannot capture all reservoir characterization in the interwell region. Therefore, as an alternative approach, seismic facies classification schemes are applied to reduce the uncertainties in the reservoir model. In this study, a machine-learning neural network was introduced to predict the lithology required for building a full-field Earth model for carbonate reservoirs in southern Iraq. The work and the methodology provide a significant improvement in facies classification and reveal the capability of a probabilistic neural network technique. Introduction The use of machine learning in seismic facies classification has increased gradually during the past decade in the interpretation of 3D and 4D seismic volumes and reservoir characterization work flows. The complete paper provides a literature review regarding this topic. Previously, seismic reservoir characterization has revealed the heterogeneity of the Mishrif reservoir and its distribution in terms of the pore system and the structural model. However, the main objective of this work is to classify and predict the heterogeneous facies of the carbonate Mishrif reservoir in a giant oil field using a multilayer feed-forward network (MLFN) and a probabilistic neural network (PNN) in nonlinear facies classification techniques. A related objective was to find any domain-specific causal relationships among input and output variables. These two methods have been applied to classify and predict the presence of different facies in Mishrif reservoir rock types. Case Study Reservoir and Data Set Description. The West Qurna field is a giant, multibillion-barrel oil field in the southern Mesopotamian Basin with multiple carbonate and clastic reservoirs. The overall structure of the field is a north/south trending anticline steep on the western flank and gentle on the eastern flank. Many producing reservoirs developed in this oil field; however, the Mid- Cretaceous Mishrif reservoir is the main producing reservoir. The reservoir consists of thick carbonate strata (roughly 250 m) deposited on a shallow water platform adjacent to more-distal, deeper-water nonreservoir carbonate facies developing into three stratigraphic sequence units in the second order. Mishrif facies are characterized by a porosity greater than 20% and large permeability contrast from grainstones to microporosity (10-1000 md). The first full-field 3D seismic data set was achieved over 500 km during 2012 and 2013 in order to plan the development of all field reservoirs. A de-tailed description of the reservoir has been determined from well logs and core and seismic data. This study is mainly based on facies log (22 wells) and high-resolution 3D seismic volume to generate seismic attributes as the input data for the training of the neural network model. The model is used to evaluate lithofacies in wells without core data but with appropriate facies logs. Also, testing was carried out in parallel with the core data to verify the results of facies classification.
Mark Egan, SPE, worked for more than 40 years with Schlumberger and its heritage companies. He held regional and global chief geophysicist positions in North America, Saudi Arabia, Dubai, and London. Egan now conducts private research using seismic modeling to determine previously unrecognized limitations of commonly accepted best practices in survey design, data processing, and inversion. Results of this work have been presented to various local geophysical societies. He holds a BS degree in physics and mathematics, an MS degree in acoustics, and a PhD degree in geophysics. Egan has authored several publications and holds two patents. He is a member of SPE, the Society of Exploration Geophysicists, the European Association of Geoscientists and Engineers, and the Geophysical Society of Houston. Egan is a member of the JPT Editorial Review Committee and can be reached at firstname.lastname@example.org.