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.
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.
Determining the optimum location of wells during waterflooding contributes significantly to efficient reservoir management. Often, Voidage Replacement Ratio (VRR) and Net Present Value (NPV) are used as indicators of performance of waterflood projects. In addition, VRR is used by regulatory and environmental agencies as a means of monitoring the impact of field development activities on the environment while NPV is used by investors as a measure of profitability of oil and gas projects. Over the years, well placement optimization has been done mainly to increase the NPV. However, regulatory measures call for operators to maintain a VRR of one (or close to one) during waterflooding.
A multiobjective approach incorporating NPV and VRR is proposed for solving the well placement optimization problem. We present the use of both NPV and VRR as objective functions in the determination of optimal location of wells. The combination of these two in a multiobjective optimization framework proves to be useful in identifying the trade-offs between the quest for high profitability of investment in oil and gas projects and the desire to satisfy regulatory and environmental requirements. We conducted the search for optimum well locations in three phases. In the first phase, only the NPV was used as the objective function. The second phase has the VRR as the sole objective function. In the third phase, the objective function was a weighted sum of the NPV and the VRR. A set of four weights were used in the third phase to describe the relative importance of the NPV and the VRR and a comparison of how these weights affect the optimized NPV and VRR values is provided.
We applied the method to determine the optimum placement of wells using two sample reservoirs: one with a distributed permeability field and the other, a channel reservoir with four facies. Two evolutionary-type algorithms: the covariance matrix adaptation evolutionary strategy (CMA-ES) and differential evolution (DE), were used to solve the optimization problem. Significantly, the method illustrates the trade-off between maximizing the NPV and optimizing the VRR. It calls the attention of both investors and regulatory agencies to the need to consider the financial aspect (NPV) and the environmental aspect (VRR) of waterflooding during secondary oil recovery projects. The multiobjective optimization approach meets the economic needs of investors and the regulatory requirements of government and environmental agencies. This approach gives a realistic NPV estimation for companies operating in jurisdiction with requirement for meeting a VRR of one.
Wu, JinYong (Schlumberger) | Banerjee, Raj (Schlumberger) | Bolanos, Nelson (Schlumberger) | Alvi, Amanullah (Schlumberger) | Tilke, Peter Gerhard (Schlumberger - Doll Research) | Jilani, Syed Zeeshan (Schlumberger Oilfield UK Plc) | Bogush, Alexander (Schlumberger)
Assessing the waterflood, monitoring the fluids front, and enhancing sweep with the uncertainty of multiple geological realisations, data quality, and measurement presents an ongoing challenge. Defining sweet spots and optimal candidate well locations in a well-developed large field presents an additional challenge for reservoir management. A case study is presented that highlights the approach to this cycle of time-lapse monitoring, acquisition, analysis and planning in delivery of an optimal field development strategy using multi-constrained optimisation combined with fast semi-analytical and numerical simulators.
The multi-constrained optimiser is used in conjunction with different semi-analytical and simulation tools (streamlines, traditional simulators, and new high-powered simulation tools able to manage huge, multi-million-cell-field models) and rapidly predicts optimal well placement locations with inclusion of anti-collision in the presence of the reservoir uncertainties. The case study evaluates proposed field development strategies using the automated multivariable optimisation of well locations, trajectories, completion locations, and flow rates in the presence of existing wells and production history, geological parameters and reservoir engineering constraints, subsurface uncertainty, capex and opex costs, risk tolerance, and drilling sequence.
This optimisation is fast and allows for quick evaluation of multiple strategies to decipher an optimal development plan. Optimisers are a key technology facilitating simulation workflows, since there is no ‘one-approach-fits-all' when optimising oilfield development. Driven by different objective functions (net present value (NPV), return on investment (ROI), or production totals) the case study highlights the challenges, the best practices, and the advantages of an integrated approach in developing an optimal development plan for a brownfield.
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.
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.
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 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.
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.
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.