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.
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.
Both oil and gold are commodities with price in US Dollars, but they choose different path in trend figure. While gold has been showing great stability over the years, oil keeps changing in price level. Oil price movements have distorted measurement of economic variables measured in dollar values. In economical evaluation for oil and gas field development projects longer than one year, oil price is one of the most critical assumptions.
This paper is trying to solve whether:
• gold is more stable than US dollars or other currencies
• gold equivalency is more reliable way to project the future costs/price
• the gold-based oil price can be applied in current economical evaluation template for justification of approval process on field development plan
Considering crude oil prices are moving dynamically for last decade, this paper exercise the model to determine realistic oil price assumption by using more stable "currencies??, thus it can provide more reliable and accurate economical evaluation. It shows that gold-based inflation-adjusted crude-oil price is preferable indampening or mitigating:
• effect of dynamic oil price nature
• impact of inflation
• risks of paper-based currency fluctuation
• discount rate requirement
Using case study of Indonesian Production Sharing Contract (PSC) fiscal terms, gold-based oil price provides more simple economical evaluation, resulting real net cashflow of field development plan. The paper concludes by demonstrating using gold equivalency instead of paper-based currencies provides more consistent and reliable nominal revenue in both perspective of PSC Contractor and Government.
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.
Fan, Zifei (Petrochina Research Institute of Petroleum Exploration and Development) | Yang, Xuanyu (China University of Petroleum) | Xue, Xia (China National Oil and Gas Exploration and Development Corporation) | Xu, An Zhu (PetroChina E&P Co) | He, Ling (Petrochina Research Institute of Petroleum Exploration and Development) | Zhao, Lun (Petrochina Research Institute of Petroleum Exploration and Development) | Mu, Longxin (Petrochina Research Institute of Petroleum Exploration and Development)
The well patterns and pattern types of well placement issue in a productive formation is an important aspect of the effective field development. The problem solution is impossible on the intuitive level due to the reservoir inhomogeneity. At present the well pattern is accepted to be located basing on the famous criteria, specialist experience and hydrodynamical simulation on a reservoir model. The designer should analyze many field development variants with different well spacing during limited time interval. The adjustment of large-scale multiwell field-development projects is challenging because the number of adjustment variables and the size of the search space can become excessive. This difficulty can be circumvented by considering well patterns and then optimizing parameters associated with pattern type and geometry. In this paper, we introduce a new framework for accomplishing this type of adjustment for vertical two or three reservoirs.The development of vertical multiple reservoirs were usually by a separate well pattern for every reservoir, or through reservoir-by-reservoir from bottom to top by only one well pattern. A separate well pattern for every reservoir requires drilling many more wells and higher investment costs, while development through reservoir-by-reservoir from bottom to top by one well pattern made oil recovery rate and development efficiency very low and uneconomic. Consideration on fully developing every reservoir well efficiently, firstly, an inverted-nine well pattern was designed for every reservoir and the well space was L (L was defined as an optimal well space for respective reservoir) and the distance between adjacent well patterns was L. Secondly, all wells were drilled to the bottom of the lowest reservoir. Thirdly, when average water-cut of producers in every two well patterns was greater than 80%, the two well patterns interchanged reservoirs. Finally, when all reservoir interchange was completed, every reservoir was developed by the new equivalent infilled well pattern with well space of L. The adjustment strategy made the required number of drilling wells in the whole field can be reduced by 50% and achieved better development effect. This strategy was put into practice on North Buzachi oil field in Kazakhstan and average oil rate of single well was increased by 20%, oil recovery rate has an increment by 12 percent, the recovery factor was increased by 6.7%, economic profit is 1.8 times that of one separate well pattern for every reservoir, the effect was perfect. This work analyzed the performance of this new strategy of well pattern design and adjustment to effectively develop vertical multiple series of reservoirs and the methods to determine the reasonable time of two well patterns interchanging reservoirs through simulation study and current application effects.
The North Kuwait Jurassic Gas (NKJG) reservoirs are currently under development by KOC with assistance from Shell under an Enhanced Technical Services Agreement (ETSA). The fractured carbonate reservoirs contain gas condensate and volatile oil at pressures up to 11,500 psi with 2.5% H2S and 1.5% CO2. This paper describes the planning and implementation of a Well Integrity Management System (WIMS) that allows the safe management of the wells that are being drilled in this hazardous environment.
The wells are designed and constructed in accordance with KOC standards and on transfer of ownership from Deep Drilling Group to Production Services Group have their integrity managed under WIMS. The system is a structured process, relating the frequency and extent of routine monitoring and testing to the particular risks associated with the wells. Compliance with WIMS requirements are routinely reported so that all are aware of the current state of well integrity. WIMS is initially managed through simple spreadsheets and during 2012 is being integrated into KOC's Digital Field infrastructure.
Initially, WIMS has been applied to the range of wells ‘owned' by Production Services Group and tests currently carried out by Well Surveillance Group under PSG's direction. In order to realise the full assurance of safe operation the scope of WIMS application is being extended to the full well population, including suspended wells, and the full range of tests required.
Implementation of WIMS will allow KOC (NKJG) to be able to state that ‘our wells are safe and we know it'.