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Quantitative integrated ground models are a requirement for proper cost optimal site characterization, for offshore renewables, coastal activities and O&G projects. Geotechnical analyses and interpretations often rely on isolated 1D boreholes. On the other hand, geophysical data are collected in 2D lines and/or 3D volumes. Geophysical data therefore provides the natural link to re-populate geotechnical properties found in the 1D boreholes onto a larger area and thereby build a consistent and robust ground model. The geophysical data can be used to estimate geotechnical data and, as of today, there are a few methods available that can reliably map the dynamic properties from the seismic data (stratigraphic information,
We have developed a workflow to build quantitative ground models following three approaches: (i) a geometric model in which the seismic data interpretations guide the prediction of geotechnical properties; (ii) a geostatistical approach in which in addition to the structural constraints, we used the seismic velocities to guide the prediction; and (iii) a multi-attribute regression using an artificial neural network (ANN). We apply it to a set of publically available data from the Holland Kust Zuid wind farm site in the Dutch sector of the North Sea. The result of the workflow yields maps or sub-volumes of geotechnical or geomechanical properties across the development site that can be used in further planning or engineering design.
In this study, we use the tip resistance from a CPT as an example. The tip resistance derived using all methods generally give good results. Validation against randomly selected CPT shows good correlation between predicted and measured tip resistance. The ANN performs better than the geostatistical approach. However, these two approaches require good data quality and a rather large dataset to be effective. Therefore, using a global dataset not restricted to the Holland Kust Zuid site may improve the prediction. Moreover, using existing empirical correlation and calibration through laboratory testing or by training another ANN model, the geotechnical stiffness/strength parameters such as angle of friction or undrained shear strength could be derived.
The next step is to use the results and their uncertainty into a cost assessment for the given foundation concepts.
The democratization of artificial intelligence began with the collection of large datasets and the ability to consume them for inferences and prediction by leveraging exponentially increasing computational power. This was further enhanced by the ability to parallel process datasets by breaking them up into independent units and applying fast computation to those units using Graphical Processing Units (GPUs). We discuss one such application in the area of seismic interpretation in the oil and gas sector. Seismic interpretation is particularly suitable since seismic datasets’ characteristics make them inherently amenable to parallel processing in post-stack format.
True big data in the oil and gas sector exists in the seismic arena, where up to one terabyte of data or more can be collected per hour. We show that while preprocessing is required for cleansing and quality checking the data, novel techniques can be applied from the medical and healthcare sectors, namely radiology, for image processing and anomaly detection in images. Further, we also show methods of preprocessing the seismic data, development of seismic images, and novel denoising techniques that lead to the construction of seismic cubes.
3D and 4D seismic cubes post-stack are today amenable to a plethora of neural network based parallel processing methods for anomaly detections. Using such techniques, the achievable speeds to detect anomalies are effectively above ten times what an experienced human being can do. These methods, at their very best, go far beyond human capabilities in terms of processing terabytes to petabytes of data. Such image processing is in use in image recognition, for example, in border control for person identification and verification, and satellite image analysis to discover the minutest of details. We showcase novel techniques based on Convolutional Neural Networks and Deep Neural Networks being utilized for subsurface geological and geophysical properties identification. Further developments of our current and future work are also discussed.
Our presentation specifically describes methods based on Convolutional and Deep Neural Networks to predict faults and salt domes in seismic images. The ability of Deep Neural Networks to continuously learn and self-optimize is the basis of our novel approach. A common criticism of machine learning methods is that most reported results describe results on field data where part of the field data has been used in training the neural network algorithms being used. In the work reported here, we describe the results of our algorithm on two completely blind field data sets – where none of the field data has been used in training the algorithms.
The Jasmine field is one of several ConocoPhillips-operated assets in the UK North Sea. ConocoPhillips is preparing to sell its fields in the North Sea, according to a Reuters report. Citing sources in the oil and gas industry and in banking, the news agency said the operator’s decision is part of an overall effort to focus on US shale operations. ConocoPhillips has operated in the North Sea for more than 45 years, with significant developments in both the UK and Norwegian sectors of the North Sea. These developments include fields in Greater Britannia, the J-Area, and the Southern North Sea fields in the UK, along with the Greater Ekofisk Area in Norway.
The Jasmine field is one of several ConocoPhillips-operated assets in the UK North Sea. Citing sources in the oil and gas industry and in banking, the news agency said the operator’s decision is part of an overall effort to focus on US shale operations. While ConocoPhillips has not launched a formal process or appointed a bank to manage a potential sale, the report said that the company’s executives have had discussions with a number of North Sea operators and bankers in the region to test the waters. Reuters said that the sale could net as much as $2 billion, though it was not clear how much of ConocoPhillips’ portfolio would be available, or if the company would put its Norwegian North Sea assets up as well. ConocoPhillips has operated in the North Sea for more than 45 years, with significant developments in both the UK and Norwegian sectors of the North Sea.
Pongtepupathum, W. (Imperial College London) | Williams, J. (British Geological Survey) | Krevor, S. (Imperial College London) | Agada, S. (Imperial College London) | Williams, G. (British Geological Survey)
This paper focuses on pressure management via brine production optimisation to reduce reservoir pressure buildup during carbon dioxide (CO2) sequestration using a geocellular model representing a sector of the Bunter Sandstone Formation. The Bunter Sandstone is a deep saline aquifer with high reservoir quality and is a leading candidate for potential CO2 capture and storage (CCS) in the UK. Brine production optimization during CO2 sequestration is necessary because it helps minimize brine waste and well construction and operational costs. In this paper, various sensitivity analyses were performed investigating well geometry, injection and production well spacing, pressure management and boundary condition effects. Two scenarios were investigated and development plans were proposed for annual injection of 7 MT/yr CO2 (Scenario 1), which is equivalent to the CO2 emissions of a 1.2 GW coal-fired power plant, and for scenario 2, where we aim to utilize the maximum storage capacity of the reservoir model. Three pressure management schemes were compared for each scenario: no pressure management or no brine production, passive pressure management where pressure relief holes are drilled and brine passively flows to seafloor without external energy, and active pressure management where brine is actively pumped out. Brine production rate and relief well patterns were evaluated and optimised. The results show that well perforation length and the use of deviated wells have a significant impact on injectivity improvement whereas well radius has little impact on injectivity. Symmetrical well placements between injection and production wells yields higher storage capacity than asymmetrical ones, and increasing the number of relief wells improves CO2 storage capacity. In the case of open boundary conditions, no pressure management is required because the reservoir quality enables pressure dissipation, resulting in a pressure buildup of less than 5 bars. In the case of closed boundary conditions, either passive or active pressure management is required to prevent seal failure from overpressurization of the reservoir and it also increases storage capacity. The cases with open boundaries, as expected, yield higher storage capacity than the cases with closed boundaries. In scenario 1, or assumed annual injection of 7 MT, storage capacity is 344 MT without pressure management and with open boundaries. This is compared to 332 and 328 MT for cases with closed boundaries and passive and active pressure management, respectively. In scenario 2, the maximum storage capacity of the model is 684 MT with no pressure management and open boundaries, and 504 and 683 MT with closed boundaries with passive and active pressure management, respectively. The storage efficiency ranges from 1 to 6% in scenario 1 to the highest at 12% in the maximum storage capacity case. In addition, three aquifer sizes; open boundary aquifer size of 2.43×1012m3, an aquifer size of 1.8×1010m3 based on pressure recharge studies of the Esmond Gas Field, and closed boundary aquifer size of 1.22×1010m3, were compared for the optimised cases. The study shows that aquifer size has an impact on estimation of CO2 storage capacity. The storage capacities of the three aquifer size cases ranging from the largest to smallest without pressure management are 344, 105 and 74 MT, respectively.
Mahrous, Ramy (Halliburton) | Vader, Ronald (Halliburton) | Larreal, Enrique (Halliburton) | Navarro, Raul (Halliburton) | Salmelid, Bjarne (Halliburton) | Honey, Alastair (Nederlandse Aardolie Maatschappij B.V.) | Weir, Malcolm (Nederlandse Aardolie Maatschappij B.V.) | Lammers, Gert (Nederlandse Aardolie Maatschappij B.V.) | Rijnen, Peter (Nederlandse Aardolie Maatschappij B.V.)
For decades, wells targeting the Rotliegend reservoir in the Southern North Sea Basin have been drilled using conventional water-based mud (WBM) in the top hole section and oil-based mud (OBM) systems throughout the remaining sections of the well. The standard well design generated high waste disposal costs onshore and offshore, particularly with regard to OBM waste. This study evaluates alternative fluid systems to help reduce disposal costs for the operator.
As part of the operator's environmental improvement strategy, the operator and fluids provider team identified potentially significant waste disposal cost savings for an onshore trial. Using a WBM system for drilling top holes as well as through the lower sections could result in cost savings through the reduction of top hole fluid dilution as well as a reduction in waste disposal costs.
A high-performance water-based mud (HPWBM) system with similar performance to an OBM system was proposed as part of a trial to demonstrate these potential savings in disposal costs for an onshore well.
The field trial was a great success compared to conventional fluid systems and methodologies. The well was drilled 11.6 days ahead of schedule and 20% under the planned budget. The time vs. depth curve was on par with what was expected when drilling with an OBM system.
The HPWBM system created a saving of >5% of the total well cost and it was 16% less expensive than conventional fluid systems. A further saving of 2.5% of the total well cost was identified for future onshore/offshore applications of the HPWBM system. It was also theorized that a further reduction in waste disposal costs could be realised in offshore operations.
The field trial was based on a basic onshore well trajectory as a proof of concept. Upon the success of using HPWBM in the basic well, more challenging onshore as well as offshore applications would be examined which have the potential to double the cost savings generated.
This novel approach of using an environmentally acceptable HPWBM system in the Southern North Sea Basin can offer significant cost saving opportunities with regard to waste management for both onshore and offshore wells compared to conventional WBM and OBM systems.
Finding and starting a new career can be a fairly straightforward process for some or a daunting task for others. The transition may start in the mind when one begins to first foster thoughts about it. A significant change in direction, particularly when it breaks free of the status quo imposed by a peer group, often comes with its share of self-doubt lurking in the corner. However, when one does go through with the career transition, there are often rewarding results, although sometimes not in ways initially envisaged. You will read about how the transitions improved the careers and lives of the individuals, the motivation behind the change, and what had to be sacrificed to see it through. Read on to see how previous experiences help with career transitions, what sorts of resources were invaluable in managing the transition, and the soft skills that were relevant in ensuring that the career transition was a success. I believe that one of my biggest professional transitions, which set me onto my current path, was switching from film and physics studies to petroleum engineering. In 2004, I was attending university in Florida.
The steel jacket that will support the Mariner A platform is towed out to sea in August. A recent one from the Petroleum Economist, headlined “Growing old gracefully,” described the future there as “squeezing out the last drops.” The global oil and gas industry is feeling the pain of the oil price plunge, but the UK feels it more acutely. Exploration drilling is at rock bottom levels, the offshore UK Continental Shelf is one of the world’s most expensive from which to produce a barrel of oil, and investment spending is expected to drop sharply in coming years. When it comes to new technology development, the North Sea is known as a global testing ground for advances in well plugging and platform removal. The pessimistic talk has served as a rallying cry for much needed change.
Description of layered reservoirs is important from a reservoir evaluation and management standpoint because layering affects primary and secondary oil recovery and large variations in permeability-thickness product or skin in different layers have great influence on well performance and production. Commingled reservoirs, where each layer has the same initial pressure without crossflow and layers may have distinct values for thickness, permeability, porosity, fracture half length and skin factor, have been investigated by many authors. Most of research work in multilayered well test analysis focus on estimating individual layer permeabilities, skin factors, fracture half length and formation pressures from well test data. But previous research work indicated that conventional buildup and drawdown(or falloff and injection) testing for wells in commingled reservoirs is only used for determining average reservoir parameters and could not be used for determining individual layer parameters in the absence of the use of the entire history of wellbore pressure and layer production.
This paper presents new testing and analysis techniques without using entire history of wellbore pressure and layer production to obtain individual layer permeabilities, skin factors, racture half length and formation pressures for a well in commingled reservoirs by using stabile flow rate data from flow profile tests acquired with production logging tools at the top of each layer before shutting-in the well and conventional pressure buildup or falloff data from the well. Before making any multilayer analysis, conventional well test analysis or type-curve analysis using log-log and derivative methods should be performed to estimate average permeability, ,fracture half length and skin factors of the total system. It can then be used as initial input values for simultaneous interpretation using an analytical model combined with nonlinear least squares estimation and type curves to estimate individual-layer permeabilities, skin factors, fracture half length and reservoir pressures. But If we use entire history of wellbore pressure and layer production, we can reduce the multiple solutions, to enhance the reliability of interpretation results.
Keywords unfractured well, fractured well, commingled reservoirs, type curve matching, well test analysis
Saraswat, Puneet (Schlumberger WesternGeco) | Raj, Vijay (Schlumberger Information Solutions) | Sen, Mrinal K. (National Geophysical Research Institute) | Narayanan, Arun (Schlumberger Information Solutions)
The 3D post-stack seismic attributes provide an intuitive and effective way of using seismic volumes for reservoir characterization and development, and further identification of exploration targets. Some of the seismic attributes can aid in the precise prediction of the geometry and heterogeneity of subsurface geological settings. These also can provide useful information on petrophysical and lithological properties when combined with well-log information. There exist numerous seismic attributes that provide a unique interpretation on some aspects of subsurface geology. Of these, the proper demarcation of structural features—such as location and edges of faults and salt domes, and their throw and extent—always has been of primary concern. In this paper, we propose new multiattribute seismic algorithms by using fractal dimension and 2D/3D continuous wavelet transform (CWT). The use of higher-dimensional wavelets incorporates information from the ensemble of traces and can correlate information between neighboring traces in seismic data. The spectral decomposition that is based on the CWT aids in resolving various features of geological interest at a particular scale or frequency, which, when rendered with fractal attribute, demarcates the boundaries between those. We apply these two algorithms separately to a seismic amplitude volume and co-render output volumes together with some weights to yield a final attribute volume incorporating information from the aforementioned algorithms. We demonstrate the efficacy of these two algorithms in terms of the resolution and proper demarcation of various geological structures on real seismic data. The application of these algorithms results in better illumination and proper demarcation of various geological features such as salt domes, channels, and faults, and it illustrates how these simple tools can help to extract detailed information from seismic data.