The time taken to safely optimise a reservoir produced by artificial lift can be measured in weeks or months.
Typically the well by well process is as follows:
• Well testing
• Amalgamation of the well test data with down hole gauge and ESP controller data
• Analysis of the data to find the existing operation conditions
• Analysis of the ESP pump curve operating point and optimisation limitations
• Sensitivity studies in software to assess the optimum frequency and WHP
• Notification for the field operations to action the changes
• Further well tests to verify the new production data.
• Analysis of the data to ensure the ESP and well are running optimally and safely at the new set points
New technology enables this process to be performed in real time across the entire reservoir or field, significantly shortening the time to increased production and enabling real time reservoir management.
Each artificially lifted well in the reservoir was equipped with an intelligent data processing device programmed with a real time model of the well. The processors were linked to a central access point where the operation of field could be remotely viewed in real time.
Each well's processor was provided with a target bottom hole flowing pressure to enable the optimum production of the reservoir. The real time system automatically compared the desired target drawdown values with the capability of the pumping system installed in each well, and automatically suggested the optimum operating frequency and well head pressure to achieve the target. Where the lift system was not capable of producing to the target bottom hole pressure, a larger pump was automatically recommended. As production conditions change the system adapted its recommended operating points to compensate and maintain target production.
This paper discusses three case studies where real time optimisation and diagnosis lead to improved production from the reservoir.
Nghiem, Long X. (Computer Modelling Group Ltd.) | Mirzabozorg, Arash (University of Calgary) | Chen, Zhangxin John (University of Calgary) | Hajizadeh, Yasin (Computer Modelling Group Ltd.) | Yang, Chaodong (Computer Modelling Group Inc.)
History matching of reservoir flow models based only on production data may not reveal deficiencies that affect future predictions. Incorporating saturation and temperature profile data that come from 4D seismic surveys in the history matching process can reduce the uncertainty of reservoir models for the prediction stage. We constructed a field reservoir model from which production history, saturation and temperature profile history were obtained. We started the history matching process with a base reservoir model, the petro-physical properties of which were substantially different than those of the field reservoir model. We propose a new methodology for matching the fluid and temperature profiles by adjusting reservoir petro-physical properties. In this methodology, some grid blocks in a reservoir model were selected judiciously to capture the overall saturation and temperature distribution profiles. In addition to well production data, we included the saturation and temperature profiles at these grid blocks as extra objective functions during the history matching process. The DECE optimization is used to reduce the objective function. We applied this method in a Steam Assisted Gravity Drainage (SAGD) process and matched the saturation and temperature profiles with an average error of less than 2%.
Mendoza, Alberto X. (ExxonMobil Neftegas) | Gaillot, Philippe (ExxonMobil Exploration Company) | Yin, Hezhu (ExxonMobil Abu Dhabi Offshore Petroleum Company) | Nicosia, Wayne (ExxonMobil Upstream Research Company) | Guo, Pingjun (Exxon Mobil Corporation) | Mardon, Duncan (ExxonMobil Upstream Research Company) | Passey, Quinn R. (ExxonMobil Upstream Research Co.) | Wertanen, Scott R. (ExxonMobil Exploration & Production Surumana) | Zhou, JinJuan (ExxonMobil Upstream Research Company) | Fitz, Dale Edward (ExxonMobil Upstream Research Co.)
Over last several years, the ability to perform accurate, quantitative formation evaluation in high-angle and horizontal (HA/HZ) wells has been increasingly recognized as a high priority, unsatisfied need within the formation evaluation (FE) community. The industry has realized that the ability to drill extended reach wells has surpassed the ability to evaluate them. Well logs are often underutilized for geologic modeling and assessment applications due to lack of confidence in petrophysical analysis results.
In this paper, we introduce a state-of-art formation evaluation toolkit specifically developed for quantitative interpretation of high angle and horizontal well logs. Starting with wellbore images and standard triple-combo field logs, the workflow consists of: 1) three-dimensional (3D) and two-dimensional (2D) display modules for well path, wellbore images logs, scalar logs and dips to quality control (QC) the data; 2) a comprehensive image analysis module combined with log analysis to build a 3D geometrical earth model; 3) a depth coherence processing (DCP) module to effectively correct recorded borehole images of different logging tool sensors with different depths of investigation (DOI) back to borehole size (BS); 4) a 3D joint inversion module to accurately model and interpret gamma ray (GR), neutron, density, and resistivity logs, to build a common petrophysical earth model; and 5) an output module in which the common earth model is populated with bedding geometries and petrophysical property distributions.
The advanced formation evaluation toolkit described in this paper enables geoscientists to realize much more value than ever before from high-angle and horizontal well data, especially in thinly bedded reservoirs. The detailed description of the internal architecture and lateral petrophysical characterization of the reservoirs are essential for understanding stratigraphy and conditioning geological models. The improved estimations of the petrophysical properties yield more accurate estimates of reserves in place.
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.
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.
The need to develop new tools that allow reservoir engineers to optimize reservoir performance is becoming more demanding by the day. One of the most challenging and influential problems facing reservoir engineers is well placement optimization.
The North Kuwait field (NKF) consists of six fields containing four naturally fractured carbonate formations. The reservoirs are composed of relatively tight limestone and dolomite embedded with anhydrate and shale. The fields are divided into isolated compartments based on fault zones and supported by a combination of different fluid compositions, initial pressures, and estimated free-water levels. Due to natural complexity, tightness, and high drilling costs of wells in the NKF, it is very important to identify the sweet spots and the optimum well locations.
This paper presents two intelligent methods that use dynamic numerical simulation model results and static reservoir properties to identify zones with a high-production potential: reservoir opportunity index (ROI) and simulation opportunity index (SOI). The Petrel* E&P software platform was chosen as the integrated platform to implement the workflow. The fit-for-purpose time dependent 2D maps generated by the Petrel platform facilitated the decision-making process used for locating new wells in the dominant flow system and provided immense support for field-development plans.
The difference between the two methods is insignificant because of reservoir tightness, limited interference, and natural uncertainty on compartmentalization. At this stage, pressure is not a key parameter. As a result, unlike brown fields, less weight was given to simulated pressure, and SOI was used to select the well locations.
The results of this study show that implementing these workflows and obtaining the resulting maps significantly improve the selection process to identify the most productive areas and layers in a field. Also, the optimum numbers of wells using this method obtained in less time and with fewer resources are compared with results using traditional industry approaches.
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.
Haider, Bader Y.A. (Kuwait Oil Company) | Rachapudi, Rama Rao Venkata Subba (Kuwait Oil Company) | Al-Yahya, Mohammad (Kuwait Oil Company) | Al-Mutairi, Talal (Kuwait Oil Company) | Al Deyain, Khaled Waleed (Kuwait Oil Company)
Production from Artificially lifted (ESP) well depends on the performance of ESP and reservoir inflow. Realtime monitoring of ESP performance and reservoir productivity is essential for production optimization and this in turn will help in improving the ESP run life. Realtime Workflow was developed to track the ESP performance and well productivity using Realtime ESP sensor data. This workflow was automated by using real time data server and results were made available through Desk top application.
Realtime ESP performance information was used in regular well reviews to identify the problems with ESP performance, to investigate the opportunity for increasing the production. Further ESP real time data combined with well model analysis was used in addressing well problems.
This paper describes about the workflow design, automation and real field case implementation of optimization decisions. Ultimately, this workflow helped in extending the ESP run life and created a well performance monitoring system that eliminated the manual maintenance of the data .In Future, this workflow will be part of full field Digital oil field implementation.
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.
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.