Alusta, Gamal Abdalla (Heriot-Watt University) | Mackay, Eric James (Heriot-Watt University) | Collins, Ian Ralph (BP Exploration) | Fennema, Julian (Heriot-Watt University) | Armih, Khari (Heriot-Watt University)
This study has focused on the development of a method to test the economic viability of Enhanced Oil Recovery (EOR) versus infill well drilling where the challenge is to compare polymer flooding scenarios with infill well drilling scenarios, not just based on incremental recovery, but on Net Present Value as well.
In a previous publication (Alusta et al., 2011, SPE143300) the method was developed to address polymer flooding, but it can be modified to suit any other EOR methods. The method has been applied to a synthetic scenario with constant economic parameters, which has demonstrated the impact that oil price can have on the decision making process.
The method was then applied and tested (Alusta et al., 2012, SPE150454) with varied operational and economic parameters to investigate the impact in delaying the start of polymer flooding to identify whether it is better to start polymer flooding earlier or later in the life of the project. Consideration was also given to the optimum polymer concentration, and the impact that factors such as oil price and polymer cost have on this decision. Due to the large number of combined reservoir engineering and economic scenarios, Monte Carlo Simulation and advanced analysis of large data sets and the resulting probability distributions had to be developed.
In this paper the methodology is applied to an offshore field where the choice has already been made to drill infill wells, but where we test the robustness of the method against a conventional decision making process for which there is historical data. We do this by performing calculations that compare the infill well scenario chosen with a range of polymer flooding scenarios that could have been selected instead, to identify whether or not the choice to drill infill wells was indeed the optimum choice from an economic perspective.
We conclude from all the reservoir simulations and subsequent economic calculations that the decision to drill infill wells was indeed the optimum choice from an economic perspective.
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 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.
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.
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 directional drilling companies in oil industry usually provide well placement services using proprietary geosteering software that utilize conventional Logging-While-Drilling (LWD) data. Usually online access to the recorded logs is available to end users, but often very limited capability exists within the oil companies to test geosteering interpretations and advise. Present paper shares the case studies of some wells in which Gas-While-Drilling (GWD) data was used in conjunction with the LWD data for well placement. Furthermore, the Geosteering Module of a third party 3D Geological modeling software was used independently within the West Kuwait Fields Development group of KOC for well placement.
Well D-08 was drilled as vertical producer in a West Kuwait Marrat carbonate reservoir, produced economic quantities of oil during initial testing, but it started cutting high amount of water due to the effect of a fault. Therefore, the well was re-entered and sidetracked at a high angle, away from the fault. Similarly, the U-73 vertical well which encountered poor reservoir facies on flank of the field, was re-entered for productivity enhancement into a thin porous reservoir layer as horizontal sidetrack towards the crest. Both these wells were monitored and geosteered in near real-time using a geosteering software module which combines the overall structural framework provided by 3D geological model, along with the well log responses characteristics from offset wells, to produce a detailed pre-drill model for Geosteering. This is achieved by forward modeling to predict changes in log characters along the planned wellbore profile. The results are displayed both in vertical and measured depth domains along a 2D curtain section with formation tops parallel to the planned well azimuth.
In addition to the conventional LWD logs, the GWD logs generated from advanced gas analysis of the drilling mud were used for geosteering during drilling well D-08 and U-73 re-entry sidetrack wells. The LWD and GWD based geosteering were done independent of each other to test the efficacy of GWD method. Geosteering software and advanced mud gas data have been paired for high angle and horizontal well placement for the first time in Kuwait which successfully guided the well trajectory while drilling.
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.