The SPE has split the former "Management & Information" technical discipline into two new technical discplines:
- Data Science & Engineering Analytics
The SPE has split the former "Management & Information" technical discipline into two new technical discplines:
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Abstract An innovative optimization methodology for field development planning is presented. A new mixed integer optimizer is described. The optimization tool's "user-friendly" plug-in in a commercial reservoir characterization and simulation package is developed, and methodology applications in exploration projects are outlined. An effective methodology is developed to optimize well placement and facility options in oil fields with multiple reservoirs. The optimized field development plan is selected for individual reservoirs from various well placements, well trajectories, injection strategies, and facility scenarios significantly impacting field oil recovery. Multiple subsurface models representing uncertainties in subsurface descriptions are applied in the optimization process. An effective mixed integer optimizer is developed. The optimizer is based on sequential cycles of a) selection of "promising" scenarios changing one decision variable per simulation and b) evaluations of combinations of the "promising" scenarios using Latin Hypercube sampling. The optimization workflow is implemented as a user-friendly plug-in to a commercial package, which allows one to a) define locations and trajectories of potential wells, b) define well placement and facility scenarios, c) run optimization workflows, and d) evaluate optimization results. The developed optimization methodology is successfully applied in several exploration projects. Effectiveness and significant benefits from the optimization applications are demonstrated. This paper can bring significant benefits to the state of knowledge in the petroleum industry by a) describing the novel methodology for optimizing field development scenarios that have significant impacts on oil recovery, b) applying the new optimizer, c) implementing the optimization plug-in in a commercial package.
Wu, Bohong (Research Institute of Petroleum Exploration & Development, PetroChina) | Nie, Zhen (Research Institute of Petroleum Exploration & Development, PetroChina) | Li, Yong (Research Institute of Petroleum Exploration & Development, PetroChina) | Deng, Xili (Research Institute of Petroleum Exploration & Development, PetroChina) | Ma, Ruicheng (Research Institute of Petroleum Exploration & Development, PetroChina) | Xu, Jiacheng (Research Institute of Petroleum Exploration & Development, PetroChina)
Abstract Marginal reserves are an important play in future energy development. Based on the statistics of China National Petroleum Corporation (CNPC), the low permeability and unconventional reservoirs occupied 92% of newly found proven reserves in China. To overcome challenges such as poor reservoir conditions, weak natural energy, low displacement efficiency, and insufficient single well production, CNPC has conducted years of research and operation to cost-effectively develop China's marginal reserves. To develop the marginal fields economically, it is required to maximize single well production, recovery and reservoir sweep with minimum CAPEX and OPEX reasonably. The production enhancement is realized by 3 key technologies, namely, sweet spot identification, multi-layered 3D short spacing horizontal well pattern, and volumetric fracturing techniques. The cost reduction is achieved by the full life cycle practice of utilizing "large cluster, factory" well design and field operation, drilling prognosis optimization, integrated intelligent surface system, and unmanned operation. CNPC cost-effective development mode is practical and successful, marginal fields characterized with heterogeneous, multi-layered oil-bearing intervals with poor continuity are being economically developed in China. By comprehensive geological study, fit-for-purpose technologies application, and geoscience-to-engineering integration, the fracture control degree of horizontal wells increased from 60% to more than 90% based on micro-seismic events, stimulated reservoir volume (SRV) increased by 46.8%, average cumulative oil production per well is more than 100 times than original production in the field. Fast and early cash flow is realized by minimum production facilities. The average drilling cycle is shortened by 61%, the surface facility construction time is reduced by 65%, and the average single well investment is reduced by 42%.
Abstract Optimization of production networks is key for managing efficient hydrocarbon production as part of closed-loop asset management. Large-scale surface network optimization is a challenging task that involves high nonlinearity with numerous constraints. In existing tools, the computational cost of solving the surface network optimization can exponentially increase with the size and complexities of the network using traditional approaches involving nonlinear programming methods. In this study, we accelerate the large-scale surface network optimization by using a distributed agent optimization algorithm called alternating direction method of multipliers (ADMM). We develop and apply the ADMM algorithm for large-scale network optimization with over 1000 wells and interconnecting pipelines. In the ADMM framework, a large-scale network system is broken down into many small sub-network systems. Then, a smaller optimization problem is formulated for each sub-network. These sub-network optimization problems are solved in parallel using multiple computer cores so that the entire system optimization will be accelerated. A large-scale surface network involves many inequality and equality constraints, which are effectively handled by using augmented Lagrangian method to enhance the robustness of convergence quality. Additionally, proxy or hybrid models can also be used for pipe flow and pressure calculation for every network segment to further speed up the optimization. The proposed ADMM optimization method is validated by several synthetic cases. We first apply the proposed method to surface network simulation problems of various sizes and complexities (configurations, fluid types, pressure regimes, etc.), where the pressure for all nodes and fluxes in all links will be calculated with a specified separator pressure and reservoir pressures. High accuracy was obtained from the ADMM framework compared with a commercial simulator. Next, the ADMM is applied to network optimization problems, where we optimize the pressure drop across a surface choke for every well to maximize oil production. In a large-scale network case with over 1000 wells, we achieve 2X – 3X speedups in computation time with reasonable accuracy from the ADMM framework compared with benchmarks. Finally, we apply the proposed method to a field case, and validate that the ADMM framework properly works for the actual field applications. A novel framework for surface network optimization was developed using the distributed agent optimization algorithm. The proposed framework provides superior computational efficiency for large- scale network optimization problems compared with existing benchmark methods. It enables more efficient and frequent decision-making of large-scale petroleum field management to maximize the hydrocarbon production subject to numerous system constraints.
Saipem announced on 18 May that it has secured two new offshore contracts with an overall value of about 850 million. Turkish Petroleum Offshore Technology Center (OTC) awarded the Italian contractor an engineering, procurement, construction, and installation (EPCI) contract for the second phase of the Sakarya FEED and EPCI Project. The work entails the EPCI of a 175-km-long, 16-in. The offshore operations are to begin in summer 2024 and will be conducted by Saipem's flagship vessel Castorone. Saipem recently completed the first phase of the Sakarya Gas Field Development project that was awarded by Turkish Petroleum OTC in 2021.
Summary Well placement optimization is one of the most crucial tasks in the petroleum industry. It often involves high risk in the presence of geological uncertainty due to a limited understanding of the subsurface reservoir. Well placement optimization is different from decision selection as countless alternatives are impossible to be enumerated in a decision model (such as the mean-variance model). In many practical applications, the decision criterion of well placement optimization is based on maximizing the risk-adjusted value (mean-variance optimization) to capture different risk attitudes. This approach regards variance as the measure of risk, and it is performed under the expected utility framework. However, investors only dislike the downside volatility below a certain benchmark. The downside-risk approach has been discussed in previous studies, in this paper, it will be introduced in the well placement optimization and discussed under the expected utility framework. It is demonstrated in a synthetic reservoir model with the consideration of spatial heterogeneity, and the comparison between the downside-risk optimization and mean-variance optimization is also presented in this example. The observation implies that well placement optimization is heavily influenced by individuals’ preference to risk. The downside-risk optimization outperforms the mean-variance optimization because it explicitly assesses risk and does not penalize high outcomes.
Sorgard, Eirik (Shell Exploration & Production Company) | Oko, Elizabeth Anne (Shell International Exploration & Production Inc) | Baird, John Isaac (Shell Exploration & Production Company) | Greenaway, Jason Alexander (Shell Exploration & Production Company) | Rabei, Rob Ibrahim (Shell Exploration & Production Company) | Pillai, Pradeep (Shell Exploration & Production Company) | Fresquez, Stacy Marie (Shell International Exploration & Production Inc)
Abstract The Vito field is located in 4,100 feet of water producing from reservoirs nearly 30,000 feet below sea level. Vito was discovered in 2009 approximately 135 miles southwest of New Orleans, Louisiana. The project underwent major field development strategy change to remain competitive in 2015 oil price environment and price resiliency going forward. The Vito project was seen as a strategic fit to the operator's existing Mars Corridor. The original Vito development strategy was to build a clone of the mega-project of Appomattox to maximize Net Present Value and Ultimate Recovery. However, as the market changed vastly in 2015, the project team refreshed the design concept to focus on capital efficiency. This paper provides an overview of the overall revised Field Development Concept of Vito. Vito has best in class resource density when compared to other Gulf of Mexico fields, which allows for a compact field development of 8 subsea wells at a single drill center. This allowed the project to not include a drilling rig on the host platform and instead deploy a new generation Deepwater rig for drilling and completions. There is severe depletion drilling risk on Vito which led the project to drill and complete all 8 wells prior to first oil. To improve ultimate recovery with low capital efficiency in well bore gas lift was included in the design. In addition, the Mars Corridor export system was looked at and required debottlenecking on both the oil and gas side. This paper is part of a Vito Project series at OTC 2023, and the other papers are listed in the references.
Otchere, Daniel Asante (Universiti Teknologi PETRONAS) | Latiff, Abdul Halim Abdul (Universiti Teknologi PETRONAS) | Taki, Mohamed Yassir (BluMarbl, Netherlands) | Dafyak, Longtong Abedenego (University of Nottingham)
Abstract More than 40 billion tonnes of CO2 are released annually, hampering climate change efforts. The goal of current research is to utilise these gases in generating energy. The oil and gas industry faces increasing expectations to clarify the implications of energy transitions for their operations and business models, reduce greenhouse gas emissions, and achieve the Paris Agreement and Glasgow Climate Pact targets. A solution is integrating machine learning and geothermal energy to optimise field development to reduce CO2 emissions while meeting energy demands. The study area is a simulated actual field data, with three existing geothermal doublets and six exploration wells. The development plan aims to satisfy the energy demand for two locations, D1 and D2, for the next 100 years, using geothermal energy and optimising field development plans via machine learning models as surrogate models. A pseudo-geological model was developed using limited field data to identify sweet spots for further drilling. Four separate model cases were simulated using DARTS. The time-energy data from DARTS was then used to train and test several machine learning models to serve as a proxy model to optimise the best strategy to meet the energy demand. The economic model was simulated for 20 years for the selected strategy for field development. Using an injection rate of 500 m3/day per well to validate the ML models, the best-performing model had a mean absolute error within the range of 0.6 to 1.5 MW for all the doublets. Based on the ML results, the computational power and time required for field development plan simulation were dramatically reduced, and several configurations were performed. The optimal strategy for this field comprises 7 geothermal doublets, 3 for D1 and 4 for D2. This strategy uses all available wells to avoid lost investment or excess cost when those wells are needed to complement production when decline sets in after 20 years, allowing a reliable and long-term energy supply. This strategy will achieve a net energy output of 108 MW for D2 and 82 for D1. This strategy uses machine learning energy estimation for the optimum configuration and addresses the issues of excess energy storage, uncertainty in production, and rising energy demand. The economic model was based on a fixed OPEX, an estimated Capex based on field development strategy, and an associated discount rate of 7%. The project resulted in a Levelized Cost of Energy of €11.16/MWH for 20 years whiles reducing annual CO2 emissions by about 367,000 metric tons. This study shows that geothermal energy is a crucial step toward cleaner energy. ML can speed up the energy transition by optimising geothermal field development. This research aims to reduce CO2 emissions while meeting energy needs.
Abstract As part of the Skarv field development strategy, the Ærfugl phase 2 project is a ~13km tie-back requiring an electrical heating flowline to provide reliable and cost-effective hydrate management. This paper will present the results of the in-field performance test and how they compare to the system intended basis of design then evaluate the operational advantages of such technical solution for an Operator. In production since November 2021, the operational feedback on the highly insulated Pipe-in-Pipe associated to the Distributed Temperature monitoring System (DTS) and data gathered during the performance test at first shutdown are used to confirm the system thermal & electrical heating performances as delivered, compare against the intended design basis, validate the operational benefits of the system in both passive and active modes for the Operator and quantify the operational gains during production shutdown. In close collaboration with the operator, the temperature monitoring data have been collected and processed to be correlated with the thermal insulation efficiency to validate the design envelop. In addition, several months of production feedback, it is demonstrated that the combination of the very efficient passive thermal insulation obtained using Izoflex in a reduced-pressure environment and a distributed temperature sensing system leads to reduced production risks and an increased operational flexibility The performance test data of the electrical heating system confirming the design prediction is the ultimate demonstration of the readiness of the Electrically Heat-Traced Flowline at a project scale. This is a major achievement for such technology which thanks to its improved maturity level is confirmed as playing an important role in future subsea developments to safely increase production and provide operational flexibility with low carbon emissions. This paper will show for the very first time on a sizable project the actual performance of an electrical heating pipe-in-pipe in conjunction with in-depth operational feedback on the use of a temperature monitoring system in a flowline. The associated operational gains for a particular case study are now demonstrated.
Marret, Antoine (TechnipFMC) | Helland, Torgeir (TechnipFMC) | Papore, Eddy (TechnipFMC) | Le-Naour, Frederic (TechnipFMC) | Pratt, Ed (TechnipFMC) | Vivet, Romain (TechnipFMC) | Møller, Fredrik Andreas (Neptune Energy) | Haugen, Knut Edmund (Neptune Energy)
Abstract This paper presents the foundations as well as the main outcomes of the development, industrialization, fabrication and installation of the TechnipFMC's Electrically Trace Heated Pipe-in-Pipe (ETH-PiP) 2.0 for application on the Fenja field development. The Fenja Field is located offshore mid-Norway at a water depth of approximately 324m, and consists of two separate hydrocarbon accumulations, the Pil and Bue reservoirs, with fluid properties leading to flow assurance challenges such as hydrates and wax formation. Following successful deployment of a first generation of Electrically Trace Heated Pipe-in-Pipe on the TotalEnergies (then Total) Islay Field in 2011, TechnipFMC have conducted the development and industrialization of a completely new generation of ETH-PiP 2.0. The new ETH-PiP 2.0 has higher electrical rating of 3.8/6.6kV to overcome the specificities of the Fenja field development including the long tie-back distance of 36.8km which makes Fenja the longest (and largest) ETH-PiP in the world. Subsequent to successful qualification of the ETH-PiP system, TechnipFMC has completed the manufacturing of 36.8km of ETH-PiP stalks at the Evanton spoolbase. These were then loaded out onto the Deep Energy pipelay vessel for subsea installation by reel lay. The installation was finalized in summer 2021 with the complete system being connected and tested from the Njord A platform after it returned from refurbishment in spring 2022. This paper presents the qualification, industrialization, assembly and installation of the new generation ETH-PiP 2.0 which forms part of the Fenja field development.
ExxonMobil has ended a major drilling campaign off Brazil after poor results, The Wall Street Journal reported, citing people familiar with the matter. Exxon said in an email to Reuters its initial exploration drilling program in Brazil is now complete and that it is "still engaged in Brazil and continues to pursue exploration activity in the country." The oil and gas giant suffered a sequence of drilling failures as an operator in the offshore acreage it started snapping up with partners for 4 billion in 2017, Reuters reported in 2022. Exxon changed its Brazil country head last year and has reported during presentations that it will focus efforts in the country with the Bacalhau field, a successful exploration campaign led by its Norwegian partner Equinor using drillship West Saturn. In May 2022, Exxon agreed to expand with Equinor the 8-billion Bacalhau project expected to produce 220,000 barrels of oil and gas per day.