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 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.
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.
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.
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%.
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 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.
Jahanbakhshi, Reza (Islamic Azad University) | Keshavarzi, Reza (Islamic Azad University) | Aliyari Shoorehdeli, Mahdi (K.N.Toosi University of Technology) | Emamzadeh, Abolqasem (Islamic Azad University)
Differential pipe sticking (DPS) is one of the most conventional and serious problems in drilling operations that imposes some extra costs to companies. This phenomenon originates mainly from improper mud properties, bottomhole assembly (BHA) (contacting area), still pipe time, and differential pressure between the formation and the drilling mud. Investigation on various conditions that lead to DPS makes it possible to develop some preventive treatments to avoid this problem's occurrence. In the past, statistical methods were applied in this area, but recently artificial neural network (ANN) approaches are frequently being used. ANNs have some priorities over conventional statistical methods such as the model-free form of predictions, tolerance to data errors, data-driven nature, and fast computation. On the other hand, the designed ANNs have some shortcomings and restrictions as they are developed to predict problems. In this paper, to solve most of the existing disadvantages of ANNs, a novel support-vector machine (SVM) approach has been developed to predict a DPS occurrence in horizontal and sidetracked wells in Iranian offshore oil fields. The results from the analysis have shown the potential of the SVM and ANNs to predict DPS, with the SVM results being more promising.
The steam-assisted gravity-drainage (SAGD) process is used widely to recover heavy oil and bitumen from formations in which no other recovery method has proved to be economical. It is an energy-intensive process, and because of economic and environmental reasons, solvents as additives to the injected steam are being explored currently to reduce the energy and emissions intensity of SAGD. The solvent-aided process (SAP), tested in the field and described in the literature, is one such attempt.
In the SAP, a small amount of hydrocarbon solvent is introduced as an additive to the injected steam. Thus, the viscosity of the oil is also reduced because of solvent dilution in addition to heating. The SAP can improve the energy efficiency of SAGD significantly, thus reducing the heat requirement, as shown in field trials discussed elsewhere. However, on the use of the right amount of solvent that can result in best overall performance, there is very little discussion in the literature. Because of the high cost of such solvents, there is incentive to optimize their use in SAGD. Recently, various authors have attempted to address the subject with, for example, arbitrary time-dependent schemes of solvent injections, assessing their impact on results or by treating the internal reservoir dynamics as a black box and using optimization methods, such as genetic algorithms (GAs), to estimate the optimal amount of solvent. While these approaches orient us to the problem in a context-specific manner, it is believed a generalized treatment to estimate optimal use of solvent requires a mechanism-based understanding.
The approach presented in this paper is aimed at estimating the optimal solvent in the context of SAGD. It combines the existing Butler's oil-drainage analytical models (Butler 1985, 1988, 1994) for SAGD and vapor extraction (VAPEX), which deal with heating effect and solvent-dilution effect one at a time, into one. Then, it calculates the time-dependent steam rates to maintain the predicted oil rates in conjunction with solvent rates and, thus, estimates the solvent/steam ratio (SSR) and the steam/oil ratio (SOR). The results are discussed for a few light-alkane solvents. In the process of this exercise, it is discovered that to obtain reasonable SSR and SOR, a significant amount of oil has to drain from a diffuse layer, which has a varying temperature, solvent concentration, and gas saturation (from maximum gas saturation at the injection end to zero at the vapor/liquid interface).