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.
In recent times the topic of well barrier integrity has become increasingly salient. Within the well completion arena, there have traditionally been two main alternatives for barrier plugs used for packer setting or temporary well abandonment; these are the metallic flapper or ball type isolation plugs. This paper describes the evolution of an innovative glass type barrier plug from its first appearance in the oilfield in 2004, to the deployment of third generation prototype systems into wells in the North Sea today.
Traditional ball or flapper type plug systems need to operate in two states: open and closed. This functionality typically necessitates the use of dynamic seals, which also have to compensate for the pressure differential applied across the plug. Plugs built in this manner can be prone to malfunctions in the dynamic seals and have limitations as to the pressure differentials that can be applied to them when opening. Additionally as the balls or flappers themselves are traditionally manufactured using metallic alloys, in the event that a plug fails to open the only alternative is milling, which if successful, will still leave a restriction in the well limiting options for future well interventions.
Glass barrier plugs have to operate in two slightly different states, solid or shattered. When the plug is run in hole the glass is in a solid state with pressure integrity maintained using static elastomeric seals. Once well operations have progressed to the stage when the plug needs to be opened, a preinstalled trip saver can be activated which would shatter the glass and open well communication. Operating in this manner avoids the use of dynamic seals thereby increasing plug reliability. Other major advantages are that the differential pressure applied across the plug when opening has no effect on the plugs functionality and since the plug is made out of glass, in the event of a trip saver malfunction the plug can be opened using a shoot down tool, a spear, or milled within approximately 10 minutes using a wireline tractor (Welltec, 2011) leaving a full bore ID for future well interventions.
This paper describes how BP Norway and TCO used the lessons learned from two generations of Glass Barrier Plugs (GBPs) to develop a system with increased debris tolerance, improved redundancy and a larger inner diameter.
Fracture ballooning usually occurs in naturally fractured reservoirs and is often mistakenly regarded as an influx of formation fluid, which may lead to misdiagnosed results in costly operations. In order to treat this phenomenon and to distinguish it from conventional losses or kicks, several mechanisms and models have been developed. Among these mechanisms under which borehole ballooning in naturally fractured reservoirs take place, opening/closing of natural fractures plays a dominant role. In this study a mathematical model is developed for mud invasion through an arbitrarily inclined, deformable, rectangular fracture with a limited extension. A governing equation is derived based on equations of change and lubrication approximation theory (Reynolds’s Equation). The equation is then solved numerically using finite difference method. Considering an exponential pressure-aperture deformation law and a yield-power-law fluid rheology has made this model more general and much closer to the reality than the previous ones. Describing fluid rheology with yield-power-law model makes the governing equation a versatile model because it includes various types of drilling mud rheology, i.e., Newtonian fluids, Bingham-plastic fluids, power-law, and yield-power-law rheological models. Sensitivity analysis on some parameters related to the physical properties of the fracture shows how fracture extension, aspect ratio and length, and location of wellbore can influence fracture ballooning. The proposed model can also be useful for minimizing the amount of mud loss by understanding the effect of fracture mechanical parameters on the ballooning, and for predicting rate of mud loss at different formation pressures.
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.
The purpose of history matching is to achieve geological realizations calibrated to the historical performance of the reservoir. For complex geological structures it is usually intractable to run tens of thousands of full reservoir simulation to trace the most probable geological model. Hence the inadequacy of the history-matching results frequently leads to poor estimation of the true model and high uncertainty in production forecasting. Reduced-order modeling procedures, which have been applied in many application areas including reservoir simulation, represent a promising means for constructing efficient surrogate models. Nonlinear dimensionality reduction techniques allow for encapsulating the high-resolution complex geological description of reservoir into a low-dimensional subspace, which significantly reduces number of unknowns and provides an efficient way to construct a proxy model based on the the reduced-dimension parameters.
Polynomial Chaos Expansions (PCE) is a powerful tool to quantify uncertainty in dynamical system when there is probabilistic uncertainty in the system parameters. In reservoir simulation it has been shown to be more accurate and efficient compared to traditional experimental design (ED). PCEs have a significant advantage over other response surfaces as the convergence to the true probability distribution is proved when the order of the PCE is increased. Accordingly PCE proxy can be used as the pseudo-simulator to represent the surface responses of the uncertain variables. When the objective and constraints of a reservoir model is described by multivariate polynomial functions, there are very efficient algorithms to compute the global solutions. We have developed a workflow at which incorporates PCE to find the global minimum of the misfit surface and assess the uncertainty associated with. The accuracy of the PCE proxy increases with the additional trial runs of the reservoir simulator.
We conduct a two dimensional synthetic case study of a fluvial channel as well as a real field example to demonstrate the effectiveness of this approach. Kernel Principal Component Analysis (KPCA) is used to parameterize the complex geological structure. The study has revealed useful reservoir information and delivered more reliable production forecasts.
PCE-based history match enhances the quality and efficiency of the estimation of the most probable geological model and improve the confidence interval of production forecasts.
The Middle Minagish Oolite Formation is 450 to 550 feet thick interval of porous limestone reservoir, composed of peloidal/skeletal grainstones with lesser amount of packstone, oolitic grainstone, wackstone and mudstone in Umm Gudair field, West Kuwait. It is characterized by small scale reservoir heterogeneity, primarily related to the depositional as well as diagenetic features. Capturing reservoir properties in micro scale and its spatial variation needs special attention in this reservoir due to its inherent anisotropy. Reservoir properties will depend on the level that we are analyzing on reservoir (millimeter to meter scale). Here we used Electrical Borehole Image (EBI) and Nuclear Magnetic Resonance (NMR) to capture small scale feature of Umm Gudair carbonate reservoir and compared them with core data
In present work, reservoir properties (including texture, facies, porosity and permeability) interpreted by the EBI shows good match with NMR driven properties and core data. Textural changes in image logs also match well with pore size distribution from NMR logs. Further highly porous zones which are considered either due to primary porosity or vugs match with larger pores of NMR logs and these corroborates with also core derived porosity. A good match has been observed between EBI, NMR and cored derived porosity. Permeability calculations have also been made and compared with core data. A detail workflow has been developed here to interpret reservoir properties on un-cored wells, where only low vertical resolution data is available. This technique is quite useful to identify the characters and mode of origin highly porous zones in reservoir section which are generally not identifiable by low resolution standard logs. This workflow will allow us to interpret the heterogeneity at high resolution level in un-cored wells, as results are validated with integration of EBI, NMR and core data.
The North Kuwait Jurassic Gas (NKJG) reservoirs are currently under development by KOC. The fractured carbonate reservoirs contain gas condensate and volatile oil at pressures up to 11,500 psi with 2.9% H2S and 1.5% CO2. Currently around 20 active wells are producing to an Early Production Facility (EPF-50) that falls short of achieving the desired capacity and capability to handle production efficiently.
To understand wells and field performance, an integrated system model comprising of wells, flow line and gathering system separator network was created. The setting up a model and its use is an integral subset of WRFM (Wells, Reservoir and Facilities Management) process that is essential for effectively managing the current asset and for further field development.
The application of the model is to be an enabler for wider implementation of the WRFM process in KOC and a tool to meet the following objectives:
The model has shown close approximation with field metered production and is already achieving many of its desired objectives.
This paper describes the use of integrated nodal analysis model to generate data gathering and well intervention opportunities not only to operate the facilities efficiently but understand well and reservoir behavior for input to full field development plan.
Exploration activity during the last ten years, targeting Jurassic carbonate reservoirs in North Kuwait (Fig 1), has culminated in the discovery of six major tight gas condensate fields, encompassing an area of about 1,800 sq km with a reservoir gross thickness of about 2,200 ft. These fields are the first free-gas fields in Kuwait, which were put on early production during 2008. The reservoirs are characterized with dual porosity matrix system, dominated by low porosity and permeability, in deep HP/HT conditions, with wide variety of hydrocarbon fluids ranging from volatile oil to gas condensate with sour gas. Typical per well production rates are up to 5,000 BOPD/BCPD and 10 MMSCFPD, making them an excellent commercial success.
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.