The global economy continues its journey of evolution and progression driven by industrialism as its primary force. With such a fast pace of development and recovery from several recessions over a number of years, dependency on energy sources became inevitable to satisfy the rising demand. This paper represents a proposed global energy price model that has the flexibility of modeling the energy price, using data from specific regions of the world, as well as the global energy pricing equation. The ANM (Alternate Novel Model) is presented here.
The model focuses mainly on oil price modeling, since oil accounts for more than 84% of the current world energy supply. The model duration is 50 years; starting from 1980 to 2030, model matching period from 1980 to 2011, and the prediction period is from 2012to 2030.
The modeling approach used in ANM adopts weighted averaging of individual factors and it relies on line regression technique. Therefore, future trends are being predicted based on the cyclic nature of the market and historical data "the future is reflection of the past??. ANM can then preduct the future oil prices, depending on the factors and variables that have been placed in the process for the output results.
The paper aims to propose a reliable model that accounts for most governing factors in the global energy pricing equation. All steps followed and assumptions made will be discussed in detailto clarify the working mechanism for this model and pave the road for any future modifications.
Stuck pipe has been recognized as one of the most challenging and costly problems in the oil and gas industry. However, this problem can be treated proactively by predicting it before it occurs.
The purpose of this study is to implement the two most powerful machine learning methods, Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs), to predict stuck pipe occurrences. Two developed models for ANNs and SVMs with different scenarios were implemented for prediction purposes. The models were designed and constructed by the MATLAB language. The MATLAB built-in functions of ANNs and SVMs, and the MATLAB interface from the library of support vector machines were applied to compare the results. Furthermore, one database that included mud properties, directional characteristics, and drilling parameters has been assembled for training and testing processes. The study involved classifying stuck pipe incidents into two groups - stuck and non-stuck - and also into three subgroups: differentially stuck, mechanically stuck, and non-stuck. This research has also gone through an optimization process which is vital in machine learning techniques to construct the most practical models. This study demonstrated that both ANNs and SVMs are able to predict stuck pipe occurrences with reasonable accuracy, over 85%.
The competitive SVM technique is able to generate generally reliable stuck pipe prediction. Besides, it can be found that SVMs are more convenient than ANNs since they need fewer parameters to be optimized. The constructed models generally apply very well in the areas for which they are built, but may not work for other areas. However, they are important especially when it comes to probability measures. Thus, they can be utilized with real-time data and would represent the results on a log viewer.
This paper presents a novel implementation for evolutionary algorithms in oil and gas reservoirs history matching problems. The reservoir history is divided into time segments. In each time segment, a penalty function is constructed that quantifies the mismatch between the measurements and the simulated measurements, using only the measurements available up to the current time segment. An evolutionary optimization algorithm is used, in each time segment, to search for the optimal reservoir permeability and porosity parameters. The penalty function varies between segments; yet the optimal reservoir characterization is common among all the constructed penalty functions. A population of the reservoir characterizations evolves among subsequent time segments through minimizing different penalty functions. The advantage of this implementation is two fold. First, the computational cost of the history matching process is significantly reduced. Second, problem constraints can be included in the penalty function to produce more realistic solutions. The proposed concept of dynamic penalty function is applicable to any evolutionary algorithm. In this paper, the implementation is carried out using genetic algorithms. Two case studies are presented in this paper: a synthetic case study and the PUNQ-S3 field case study. A computational cost analysis that demonstrates the computational advantage of the proposed method is presented.
Arnaout, Arghad (TDE Thonhauser Data Engineering GmbH) | Thonhauser, Gerhard (Montanuniversitat Leoben) | Esmael, Bilal (Montanuniversitat Leoben) | Fruhwirth, Rudolf Konrad (TDE Thonhauser Data Engineering GmbH)
Detection of oilwell drilling operations is an important step for drilling process optimization. If drilling operations are classified accurately, detailed performance reports not only on drilling crews but also on drilling rigs can be produced. Using such reports, the management can evaluate the drilling work more precisely from performance point of view.
Mud-logging systems of modern drilling rigs provide numerous sensors data. Those sensors measurements are considered as indicators to monitor different states of drilling process. Usually real-time measurements of the following sensors data are available as surface measurements: hookload, block position, flow rates, pump pressure, borehole and bit depth, RPM, torque, rate of penetration and weight on bit.
In this work, collected sensors measurements from mud-logging systems are used to detect different drilling operations. Detailed data analysis shows that the surface sensors measurements can be considered as a main source of information about drilling operations. For this purpose, a mathematical model based on polynomials approximation is constructed to interpolate sensors data measurements.
Discrete polynomial moments are used as a tool to extract specific features (moments) from drilling sensors data. Then we use these moments for each drilling operation as pattern descriptor to classify similar operations in drilling time series. The extracted polynomial moments describe trends of sensors data and behavior of rig's sub-systems (Rotation System, Circulation System, and Hoisting System). Furthermore, this paper suggests a method on how to build patterns base and how to recognize and classify drilling operations once sensors data received from mud-logging system. Drilling experts compare the results to manually classified operations and the results show high accuracy.
Historically, shale instability is a challenging issue when drilling reactive formations using water-based muds (WBM). Shale instability leads to shale sloughing, stuck pipe, and shale disintegration causing an increase in fines that affects the rate of penetration. To characterize shale instability, laboratory tests including Linear Swell Meter (LSM), shale-erosion and slake-durability are conducted in industry. These laboratory tests, under different flow conditions, provide shale-fluid interaction parameters which are indicative of shale instability. The composition of WBM is designed to optimize these interaction parameters, so that when used in the field the fluid helps achieve efficient drilling.
This paper demonstrates modeling of shale-fluid interaction parameters obtained from the LSM test. In the standard LSM test, a laterally confined cylindrical shale sample is exposed to WBM at a specific temperature and its axial swelling is measured with time. The swelling reaches a plateau which is characterized by a shale-fluid interaction parameter called % final swelling volume (A). A typical LSM test runs for around 48-72 hours and many tests may be needed to optimize fluid composition.
In this work, a method/model is developed to predict final swelling volume (A) as a function of the Cation exchange capacity (CEC) of the shale and salt concentration in the fluid (prominent factors affecting shale swelling). An empirical model in the form of A = f(CEC)*f(salt) which describes the explicit dependence on the influencing variables is developed and validated for 16 different shale samples at various salt concentrations. This model would significantly reduce LSM laboratory trials saving time and money. It could also enable rig personnel to obtain quick measure of shale characteristics so that WBM composition could be adjusted immediately to avoid shale instability issues.
Viscosity and Density are important physical parameter of crude oil, closely related with the whole processes of production and transportation, and are very essential properties to the process design and petroleum industries simulation. As viscosity increases, a conventional measurement becomes progressively less accurate and more difficult to obtain. According to the literature survey, most published correlations that are used to predict density and viscosity of heavy crude oil are limited to certain temperatures, API values, and viscosity ranges. The objective of present work is to propose accurate models that can successfully predict two important fluid properties, viscosity and density covering a wide range of temperatures, API, and viscosities. Viscosity and density of more than 30 heavy oil samples of different API gravities collected from different oilfield were measured at temperature range 15oC to 160oC (60oF to 320oF), and the results were used to ensure the capability of proposed and published correlations to predict the experimental viscosity and density data. The proposed correlation can be summarized in two stages. The first step was to predict the heavy oil density from API and temperature for different crudes. The predicted values of the densities were used in the second step to develop the viscosity correlation model. A comparison of the predicted and actual viscosities data, concluded that the proposed model has successfully predict all data with average relative errors of less than 12% and with the correlation coefficient R2 of 0.97, and 0.92 at normal and high temperatures respectively. Meanwhile, the results of most of the available models has an average relative error above 40%, with R2 values between 0.19 to 0.95. These comparisons were made as a quality control to confirm the reliability of the proposed model to predict density and viscosity values of heavy crudes when compared with other models.
Vibrations are caused by bit and drill string interaction with formations under certain drilling conditions. They are affected by different parameters such as weight on bit, rotary speed, mud properties, BHA and bit design as well as by the mechanical properties of the formations. During the actual drilling process the bit interacts with different formation layers whereby each of those layers usually have different mechanical properties. Vibrations are also indirectly affected by the formations since weight on bit and rotary speed are usually optimized against changing formations (drilling optimization process). Therefore it can be concluded that for optimized drilling reduction of vibrations is one of the challenges.
A fully automated laboratory scale drilling rig, the CDC miniRig, has been used to conduct experimental tests. A three component vibration sensor sub attached to drill string records drill string vibrations and an additional sensor system records the drilling parameters. Uniform concrete cubes with different mechanical properties were built. Those cubes as well as a homogeneous sandstone cube were drilled with different ranges of weight on bit and bit rotary speed. The mechanical properties of all cubes were measured prior to the experiments. During all experiments, drilling parameters and the vibration data were recorded. Based on analyses of the data in the time and the frequency domain, linear and non-linear models were built. For this purpose the interrelations of sandstone and concrete mechanical properties, drilling parameters and vibration data were modeled by neural networks. Application of sophisticated attribute selection methods showed that vibration data in both, time- and frequency domain, have a major impact in modeling the rate of penetration.
Offshore pipelines are a viable option for the safe transport ofhydrocarbons in the Arctic. For continued safe and cost efficient operation, itis important to ensure integrity as well as minimize field inspection andintervention. This can be achieved through an optimized Inspection andMaintenance (IM) program. Determining the required frequency of IM, in a costefficient manner is critical for ensuring integrity and optimizing inspectionand maintenance costs without compromising safety. For piggable lines, smartpigs are used for In-Line Inspection (ILI). A conservative approach (small IMintervals) can be costly, increases the human / Remotely Operated Vehicle (ROV)exposure and yield little new information. A strategy with too little IM canlead to unexpected failures, as too little information is acquired on thecondition of the pipeline. An optimal IM strategy based on the condition ofpipeline is developed in this paper.
In this paper, major Arctic offshore pipeline integrity challenges areevaluated. Considering these challenges, a Risk Based Integrity Modeling (RBIM)framework has been proposed. Design challenges from the effects of ice gouging,strudel scour, frost heave, permafrost thaw settlement, and upheaval bucklingcan be mitigated through proper trenching and burial, as well as conditionmonitoring during operation. The major integrity challenges during operationmay arise from the progressive structural deterioration processes and changesin the right-of-way seabed conditions. The structural deterioration processeswill include time-dependent processes such as corrosion, cracking, andpermafrost thaw settlement. Non-time dependent (random) processes, such asthird party damage, ice gouging, strudel scour, and upheaval buckling will poseadditional risk during operation, but are not addressed in this paper. Theseeffects can be partially addressed through ILI and periodic seabed surveyinspections.
The risk to an Arctic offshore pipeline will be evaluated with respect tothe deterioration processes. The risk is estimated as a combination of theprobability of failure and its consequences. The probability of failure isestimated using the Bayesian analysis. Modeling the structural degradationprocesses using Bayesian analysis is not a new concept; however, modelingdegradation processes using non-conjugate pairs is a new technique that isdiscussed in this paper. Bayesian analysis is based on the estimation of prior,likelihood, and posterior probabilities. Field ILI data is used in theanalysis. The posterior models possess better predictive capabilities of futurefailures. The consequences are estimated in terms of the cost of failure andthe planned IM program. Cost of failure includes the cost of lost product, costof shutdown, cost of spill cleanup, cost of environmental damage and liability.Cost of IM includes the cost to access the pipeline, gauge defects, and carryout inspection and necessary minimal maintenance. Implementation of theproposed RBIM will improve pipeline integrity, increase safety, reducepotential shutdowns, and reduce operational costs.
Synthetic aperture radar (SAR) has been extensively used for the derivationof valuable information regarding sea ice properties and conditions. This workfocuses on the use of RADARSAT-2 ScanSAR Wide images (500x500 km swaths with50x50 meter pixels) to provide sea ice information for operations support inthe Arctic. Our developed processes generate several products that supportnavigation and operations in ice infested waters: i) Sea ice images, i.e.delineating and mapping sea ice relative to the open water, ii) Seasonal trendcharts of sea ice over an area of interest and, iii) Automated ice featuretracking and pressure zone mapping.
Using the RADARSAT-2 dual-polarization images and automated techniques, seaice maps are generated to identify regions of open water and of sea ice. Fromthe sea ice maps, total ice concentration is derived and combined withhistorical concentration maps. The output seasonal trend charts can be used toassist in monitoring Arctic sea ice extent and sea ice identification to aidwith navigational safety operations. Finally, we develop an automated icefeature tracking that can track moving ice and from which pressure and driftzones are identified. Future work will involve the development of theprediction of movement of ice floes and packs, using the ice feature trackingtechnique as the foundation.
Davidson, Malcolm (European Space Agency) | Walker, Nick (European Space Agency) | Williams, Chris (eOsphere) | Power, Desmond (C-CORE) | Ramsay, Bruce (Consultant) | Partington, Kim (Polar Imaging Limited) | Barber, David (University of Manitoba) | Arkett, Matt (Canadian Ice Service) | De Abreu, Roger (Canada Centre for Remote Sensing)
In conducting safe and cost effective operations in ice prone waters, icemanagement and risk mitigation practices are integral to operations. A criticalelement in ice management is the mapping and characterisation of sea ice.Satellite synthetic aperture radar (SAR) is a standard tool used by icecharting agencies to map the extent of sea ice. Wide-swath SAR has become thepreferred sensor of choice for ice mapping and the collection of data regardingice parameters. SAR provides a high degree of information content on basic iceparameters such as concentration, type and topography. SAR can be used tocharacterise different sea ice types, such as multi-year versus first year ice,and the use of multiple SAR frequencies (L, C and X-Band) can reduceinterpretation ambiguities during the melt season. The advent ofmulti-frequency and polarization SAR systems, acting as a constellation, isseen as an important next step in the evolution of sea ice monitoring. Theevaluation of a SAR ice constellation is an interesting challenge since aquantitative evaluation is necessary. As a consequence, a sea ice backscattertool has been developed that provides a figure of merit estimation of iceclassification from a constellation scenario. The authors have used its sea-icebackscatter tool to simulate various ice constellation scenarios. Thesescenarios will be presented in the context of their utility and versatility inoil and gas operations. The implementation of a SAR ice constellation providesthe opportunity to significantly expand the ice information extractioncapabilities, over and above that of these systems acting alone. In the contextof its use within Arctic resource development, SAR constellations offerenhanced ice charting to the oil and gas industry.
Index Terms— Sentinel-1, SAR, sea-ice, backscatter,constellations