Tyrie, Jeb (Bridge Petroleum) | Mulcahy, Matt (Bridge Petroleum) | Leask, Robbie (Bridge Petroleum) | Wahid, Fazrie (Bridge Petroleum) | Arogundade, Olamide (Schlumberger) | Khattak, Iftikhar (Schlumberger) | Apena, Gani (Schlumberger) | Samy, Mohammed (Schlumberger) | Sagar, Rajiv (Schlumberger) | Xia, Tianxiang (TRACS International) | Nyadu, Kofi (WorleyParsons, Advision) | Maizeret, Pierre-David (Schlumberger)
This paper describes the proposed re-development of the Galapagos Field, comprising the abandoned NW Hutton field and the Darwin discovery (Block 211/27 UKCS) which forms a southerly extension. The paper covers the initial concept and analytical evaluation, the static uncertainty model build, the dynamic model history-match, the iterations between static and dynamic modelling, the development subsea and well locations, the optimisation workflow of the advanced Flow Control Valve (FCV) completions in both producers and injectors and the facilities constraints.
The redevelopment plan involved several multi-disciplinary teams. 20 years of production data from 52 wells were analysed to identify the production behaviour and confirm the significant target that provided the basis for the development concept selection. The full Brent sequence compartmentalised stochastic static model was based on reprocessed seismic plus 14 exploration and appraisal wells. Streamlines, uncertainty sensitivities and mostly good detective work honed a history match to RFT, BHP, PLT and oil and water production. P50, P90/P10 models were selected and over 100 FCVs optimised to deliver the profiles against an identified FSPO facilities’ constraints.
Over 1,000 static models were delivered consisting of sheet sands, incised valleys and channels in heterolithic facies overprinted by a depth trend with appropriate uncertainty ranges. The high well count gave a tight STOIIP probabilistic range of 790/883/937 million stb. The early RFTs illustrated extreme differential depletion between Brent zones and subzones of the Ness. To history-match these the dynamic model retained the static model definition in the Upper Ness to capture the thin but extensive shales. The early 18-month depletion and the late steady production-injection phases were simulated separately in prediction mode and matched the Production Analysis estimated ‘future’ production giving confidence to the history matched model. The initial concept development of 4 subsea-centres, to cover the large field area, with an injector in each compartment proved a robust selection. The horizontal wells increase PI where needed and mitigate internal faulting. The optimisation of the FCVs significantly increased oil production from all zones and drastically reduced water injection and production so that the identified FPSO modifications were relatively modest. The final First Stage Field Development Plan consists of 11 producers and 6 injectors across developed and undeveloped areas confirmed robust P50 reserves of 84 million boe.
Robust concept selection allowed for early identification of production units so that constraints and modifications could be accounted for within the economic model.
The Galapagos field re-development plan is an excellent example of how detailed static and fully history matched dynamic models can lay the foundations for new technology like the optimisation of the FCVs to access bypassed reserves using significantly smaller production units with reduced requirements for power, compression, gas lift, pumping pressure, injection and production. In short, they shrank the facilities.
The growing popularity of model-based optimization work flows has resulted in an increase in their application to field cases. This paper presents an unbiased stochastic data-driven work flow in which surface and subsurface uncertainties are accounted for and their effects on facilities design and operational decisions are quantified. Three-dimensional reservoir models are best created with a combination of well logs and 3D-seismic data. However, the effective integration of these results is not easy because of limited seismic resolution.
The value of hidden-danger data stored in text can be revealed through an approach that can help sort and interpret information in an ordered way not used previously in safety management. A recently launched joint industry project (JIP) is working to improve petrophysical analysis methods to reduce the time and expense of characterizing tight sandstone gas reservoirs for exploration, appraisal, and production. Failure to prioritize objectives and improper selection of candidate wells can have significant implications for both derived value and potential risk. Data mining for production optimization in unconventional reservoirs brings together data from multiple sources with varying levels of aggregation, detail, and quality. A data-driven approach to successfully analyze and evaluate production-fluid impact during facility system divert events is presented.
Production optimization has become currently one of the most critical aspects for well/reservoir management. This course will cover the following aspects: Nodal Analysis, Formation Damage, Quantification of Formation Damage, Pressure Transient Analysis, Intervention Benefit, and Artificial Lifting (ESP). Every oil company, National, International or an independent company is working on achieving their economic goals by optimizing well deliverability. In this training participants will learn how to evaluate actual well performance and how to optimize well deliverability. Fabio Gonzalez is currently a Reservoir Engineering Advisor with BP on assignment in Kuwait.
This course is designed to give trainees an overview of various artificial lift solutions and related production optimization concepts. After introducing participants to the need for an artificial lift system, training will focus on each of the following lift methods: Gas lift, Reciprocating Rod Lift, Progressing Cavity Pumping, Hydraulic Pumping, Electrical Submersible Pumping, Plunger and Capillary System. For each lift type, the course covers main components, application envelope, relative strengths and weaknesses. Animations, field cases, and example-calculations are used to reinforce concepts. A unique feature of this course is discussion on digital oil field as applicable to lift optimization.
The Caribbean nation hopes the auction will lead to at least two exploration projects in a region that has become increasingly attractive thanks to new discoveries and investments made in neighboring countries. This new development is the first to recover commercial quantities of oil in the UK from reservoirs that are generally considered non-productive. The explorer has so far encountered 400 ft of reservoir pay zone in an area where it has three other producing fields. The state-run offshore company has found a gas and condensate field that holds an estimated 250 million BOE. DEA Deutsche Erdoel is buying Sierra Oil & Gas, giving the German operator stakes in six new blocks off Mexico—including the Zama discovery, where appraisal drilling is now under way.
Content of PetroWiki is intended for personal use only and to supplement, not replace, engineering judgment. SPE disclaims any and all liability for your use of such content. A form of mathematical programming in which the objective function is a linear combination of the independent variables. The solution technique is called the simple method because it can be viewed as a search along the edges of a hypercube.
Decision tree analysis and Monte Carlo simulation are the most commonly used tools in decision and risk analysis. But other tools such as optimization, options analysis, and combinations of these various tools can also be useful. This article examines the importance of data analysis and the nature and application of these other tools. Regardless of the principal tool used in risk analysis--Monte Carlo simulation or decision trees--empirical data may play an important role. Similarly, the input distributions selected for a Monte Carlo model are easier to justify when analogous data is available to support the choices of distribution type and value of defining parameters, such as mean and standard deviation.
Baek, Seunghwan (Texas A&M University) | Akkutlu, I. Yucel (Texas A&M University) | Lu, Baoping (Sinopec Research Institute for Petroleum Engineering) | Ding, Shidong (Sinopec Research Institute for Petroleum Engineering) | Xia, Wenwu (Harding Shelton Petroleum Engineering & Technology Limited)
Routine history-matching and reservoir calibration methods for horizontal wells with multiple hydraulic fractures are complex. Calibration of important fracture and matrix quantities is, however, essential to understand the reservoir and estimate the future recoveries. In this paper, we propose a robust method of simulation-based history-matching and reserve prediction by incorporating an analytical solution of production Rate Transient Analysis (RTA) as an added constraint. The analytical solution gives the fracture surface area contributing to the drainage of the fluids from the matrix into the fractures. The surface area obtained from the RTA is the effective area associated with the production—not total area. It is the most fundamental and the most significant quantity in the optimization problem. Differential evolution (DE) algorithm and a multi-scale shale gas reservoir flow simulator are used during the optimization. We show that the RTA-based optimization predicts the quantities related to completion design significantly better. Further, we show how the estimated total fracture surface area can be used to measure the hydraulic fracturing quality index, as an indication of the quality of the well completion operation. The most importantly, we predict that the fractures under closure stress begin to close much sooner (100 days) than the prediction without the RTA-based fracture surface area constraint. The deformation continues under constant closure stress for about 20 years, when the fractures are closed nearly completely. This work attempts to use the traditional reservoir optimization technologies to predict not only the reserve but also the life of the unconventional well.
The success of an unconventional hydrocarbon development depends on effective hydraulic fracturing, which highly depends on reservoir properties and the stimulation procedure. In the beginning of shale development, the industry practice was to conduct a large number of field trials, which was a very expensive and time consuming practice. Advanced integrated studies are being performed today by industry on rock and SRV (Stimulated Rock Volume) characterization, however the topic remains still vastly challenging, due to the complex nature of fracturing in shales as well as because of the complexity of multiple physics and the number of operational parameters involved in shale development from formation characterization to SRV creation and production.
This project used a numerical simulation approach, based on truly 3D reservoir modelling of fracture network generation and stimulation, to optimize hydrocarbon production through the investigation of a large number of virtual well stimulations. Starting with a calibration workflow, taking into account potential reservoir and geomechanical uncertainties, a calibrated reservoir model was built. The goal of this approach is to find the optimal stimulation parameters much faster with much less investments compared to the industry standard of simply undertaking a trial and error well drilling and completion process. The reservoir model calibration, the multi-realization runs, together with the metamodel analyses have been performed using a workflow and a range of advanced software tools developed since 2010 (Bai et al, 2011, Gao et al, 2011, Yeh et al, 2018).
In this study, the proposed workflow was applied to a major Unconventional Oil and Gas field. A multi-stage hydraulic fracturing operation has been modelled and calibrated based on the data from a real hydraulic fracture shale gas operation. Geomechanical and geological uncertainties have been taken into account in the calibration process of the reservoir model. Furthermore, microseismic monitoring results and fracture treatment pressure data have been used to calibrate important parameters in the fracturing modelling process. Utilization of multi-realization runs while scanning parameters uncertainties, enabled to rank the parameters influencing the stimulated rock creation process.
In a second step, a sensitivity study has been performed within a predefined window of variation of operational parameters. From this sensitivity study, important operational parameters influencing fracture network geometry and related hydrocarbon production have been identified. Based on the sensitivity study, meta models were then generated which represent the influence of the variation of operational parameters on fracture network geometry and hydrocarbon production. The meta models have been combined with costs to optimize operational parameter taking into account the conflicting nature of EUR, NPV, VIR. The results of this meta model-based optimization may help improve the decision-making process of hydraulic fracturing operations and shale play development, including unit development costs and unit profitability.