Looping through an Integrated Workflow to update a Seismic Driven Reservoir Model for Field Development Optimization

Author(s):  
C. Tarchiani ◽  
F. Arata ◽  
A. Cossa ◽  
E. Paparozzi ◽  
et al.
SPE Journal ◽  
2016 ◽  
Vol 22 (02) ◽  
pp. 562-581 ◽  
Author(s):  
HanYi Wang

Summary One of the most-significant practical problems with the optimization of shale-gas-stimulation design is estimating post-fracture production rate, production decline, and ultimate recovery. Without a realistic prediction of the production-decline trend resulting from a given completion and given reservoir properties, it is impossible to evaluate the economic viability of producing natural gas from shale plays. Traditionally, decline-curve analysis (DCA) is commonly used to predict gas production and its decline trend to determine the estimated ultimate recovery (EUR), but its analysis cannot be used to analyze which factors influence the production-decline trend because of a lack of the underlying support of physics, which makes it difficult to guide completion designs or optimize field development. This study presents a unified shale-gas-reservoir model, which incorporates real-gas transport, nanoflow mechanisms, and geomechanics into a fractured-shale system. This model is used to predict shale-gas production under different reservoir scenarios and investigate which factors control its decline trend. The results and analysis presented in the article provide us with a better understanding of gas production and decline mechanisms in a shale-gas well with certain conditions of the reservoir characteristics. More-in-depth knowledge regarding the effects of factors controlling the behavior of the gas production can help us develop more-reliable models to forecast shale-gas-decline trend and ultimate recovery. This article also reveals that some commonly held beliefs may sound reasonable to infer the production-decline trend, but may not be true in a coupled reservoir system in reality.


2018 ◽  
Author(s):  
Mahanaz Hatvik ◽  
Jens Petter Nørgård ◽  
Kjartan Berg ◽  
Knut Vannes ◽  
Tine Bauck Irmann-Jacobsen ◽  
...  

2001 ◽  
Vol 4 (01) ◽  
pp. 26-35
Author(s):  
Richard W. Smith ◽  
Rodolfo Colmenares ◽  
Eulalio Rosas ◽  
Isaura Echeverria

Summary The El Furrial field is one of Venezuela's major field assets and is operated by PDVSA (Petroleos de Venezuela, S.A.), the national oil company. Its current production of more than 450,000 BOPD makes it a giant oil field. Development of the field, which has an average reservoir depth of approximately 15,000 ft, is in its mature stages owing to implementation of high-pressure gas injection. PDVSA has consistently followed a forward planning approach related to reservoir management. Using high-angle deviation drilling techniques allows development wells to be strategically located by penetrating the reservoir at high angles to optimize production rate, extend well life, increase reserves per well, reduce operating expenses, and reduce total field development costs. A reservoir model was constructed and simulated with detailed reservoir stratigraphy to determine realistic potential of high-angle wells (HAW's). Five wells had been drilled as of June 2000, and the first four wells have proved the effectiveness of the design. The philosophy, modeling technique, well design considerations, problems encountered, well results, and economic criteria provide a clear understanding of the risk of this technology not previously used at this depth in Venezuela. The result was the first HAW in the deep, challenging environment of eastern Venezuela. Results show that optimization objectives can be attained with HAW's, mainly increasing per-well production rate, maximizing per-well recovery, and extending the breakthrough time of gas or water from pressure maintenance and enhanced oil recovery projects. Well results indicate that the geological and simulation modeling technique is reliable and accurate. A pilot program shows that HAW technology provides major advantages to increase production rate and reduce the overall number of wells needed to reach production objectives. However, the project also has experienced a number of unexpected drilling problems.1 The costs associated with the total project are significant, but more importantly, this program becomes very attractive because of the long-term benefits of decreased water-cut related to current water injection; decreased gas breakthrough owing to high-pressure gas injection, and fewer wells required to meet production goals. Technical contributions include the following:The modeling technique of applying detailed stratigraphy to a full-scale reservoir model is accurate if performed with the appropriate objectives in mind.The application of state-of-the-art drilling techniques to attain high angles at deep drilling depth is possible; however, drilling problems caused by formation instability require more study and experience.This method can be applied to other fields in the eastern Venezuelan basin currently under, or planned to be under, enhanced recovery programs and development programs. Introduction The El Furrial field is one of several giant fields found northwest of Maturin, Venezuela, in what is described as the El Furrial thrust trend (location shown in Fig. 1). The field was discovered in 1986 with the FUL-1 well, which established production from the Naricual formation. A late 1996 study, using a full-field simulation model of the El Furrial field, showed that problems associated with gas or water breakthrough in producing wells from high-pressure gas injection and water injection can be reduced with this technology. The potential to reduce problems comes from drilling infill wells at a high angle between the advancing gas and water fronts. High-pressure gas injection was started in 1998 and was justified, in part, by this work and other associated studies. The field produces from two formations, the Naricual and Los Jabillos, giving a total gross thickness of more than 1,500 ft. The primary 1,200-ft-thick Naricual formation is divided into three major stratigraphic sequences - the Superior (upper), Medio (middle), and Inferior (lower). Net-to-gross ratio is typically 80%. Philosophy PDVSA has consistently maintained reservoir models through the years to aid in reservoir management.2 To date, eight full-field and numerous sector-simulation models have been built. Optimization of the field began in 1996. During the study, it was noted that predictions of conventional vertical infill wells drilled into the structure had short production lives because of water or gas breakthrough. The review identified the possibility of placing well trajectories between the advancing water and gas fronts. One benefit was that the production rate from new wells could be increased; this indicated that the number of development wells could be reduced, saving investment costs. Thus, the following objectives were determined.Define optimization alternatives of the El Furrial field well-development scheme. The use of nonconventional well completions such as vertical large interval single completions (LISC) and high-angle completion (HAC) wells may present a higher potential for meeting production needs at a lower total development cost.Define the most reasonable completion configuration for new wells in El Furrial field. It is probable that the entire Naricual acts as a single reservoir unit, with at least partial vertical communication existing in the majority of the field caused by fault juxtaposition and limited fractures associated with faults. Therefore, single completions in all of Naricual Superior and Medio, or Naricual Medio and Inferior, may present viable completion alternatives.Provide technical support to the Venezuelan Ministry of Mines and Energy, which approves operation philosophy, development, and completion practices. The HAW program was different from the previous accepted philosophy, so technical support was necessary to permit the FUL-63 pilot test well. High-Angle Wells This work was split into two parts. The first was an evaluation of HAC wells as an alternative to current vertical-well strategies. This includes the possible alternative of LISC completions for all of Naricual Superior and Medio. The second was additional simulation cases to test the potential development plan with only HAC wells in a full-scale reservoir model.


2017 ◽  
Author(s):  
S. Yu. Shtun ◽  
M. Yu. Golenkin ◽  
A. S. Shtun ◽  
D. D. Shabalinskaya ◽  
A. V. Cheprasov ◽  
...  

2021 ◽  
Author(s):  
Khor Siew Hiang ◽  
Petrunyak Volodymyr ◽  
Yevgen A. Melnyk ◽  
Prykhodchenko Oleksii ◽  
Stefaniv Viktor ◽  
...  

Abstract The adoption of an integrated asset modeling approach was explored to kick-start the corporate digital transformation strategy for its oil and gas section. Besides the integrated asset model, the digital initiatives included predictive maintenance, well performance optimization, and a flow assurance advisor aimed at daily production operations and maintenance, creating a pathway to the digital oilfield (DOF). The integrated asset model would be the main pillar of DOF realization and implementation, its offered technology aimed at short-term, medium-term, and long-term planning. The adopted well-proven integrated asset modeling methodology enabled a geological complex with a high-fidelity physics reservoir model, multiple interdependent wells, pipeline networks, process facility models to be integrated seamlessly on a single platform for validation of its existing production operation strategy and field development plan. The black-oil reservoir model was history matched, and the production network models had detailed wellbore and pipeline hydraulics calibrated with the latest well-test data. The compositional fluid modeling allowed the capture of any flow assurance issues that arose across the networks, which were mapped to the corresponding process facility models with physical specifications and operational constraints defined. A fully integrated asset model was developed for the studied asset, where liquid/vapor tables were prepared for black-oil delumping (Ghorayeb and Holmes, 2005) of the reservoir models to surface network models (Mora et al. 2015), while fluid models of both production network and process models were validated before mapping to ensure fluid fidelity. The availability of this integrated asset model with an embedded spreadsheet program incorporating some simple economic calculations allowed the flexibility of short-term production optimization and long-term asset planning, which was focused to provide all the vital valuable inputs to better field management, fast and accurate decision making, and optimum safe operation of process units in meeting the sales contract. The integrated asset model offered a platform for engineers from different domains to collaborate with aligned common operational and planning objectives. It empowered assessments of production operation strategy and field development scenarios conducted at full field level from pore to process. The customized reporting, the ability to connect to other tools, and to push results to dashboards helped to kick-start the corporate digital transformation strategy.


2010 ◽  
Author(s):  
Luhut Partumpuan Gultom ◽  
Jose Benito Corbellini ◽  
I. Made Agus Sutha Negara ◽  
Ratno Adi Harnondo ◽  
Amireno Soenoro ◽  
...  

Author(s):  
Yusuf Nasir ◽  
Jincong He ◽  
Chaoshun Hu ◽  
Shusei Tanaka ◽  
Kainan Wang ◽  
...  

Oil and gas field development optimization, which involves the determination of the optimal number of wells, their drilling sequence and locations while satisfying operational and economic constraints, represents a challenging computational problem. In this work, we present a deep-reinforcement-learning-based artificial intelligence agent that could provide optimized development plans given a basic description of the reservoir and rock/fluid properties with minimal computational cost. This artificial intelligence agent, comprising of a convolutional neural network, provides a mapping from a given state of the reservoir model, constraints, and economic condition to the optimal decision (drill/do not drill and well location) to be taken in the next stage of the defined sequential field development planning process. The state of the reservoir model is defined using parameters that appear in the governing equations of the two-phase flow (such as well index, transmissibility, fluid mobility, and accumulation, etc.,). A feedback loop training process referred to as deep reinforcement learning is used to train an artificial intelligence agent with such a capability. The training entails millions of flow simulations with varying reservoir model descriptions (structural, rock and fluid properties), operational constraints (maximum liquid production, drilling duration, and water-cut limit), and economic conditions. The parameters that define the reservoir model, operational constraints, and economic conditions are randomly sampled from a defined range of applicability. Several algorithmic treatments are introduced to enhance the training of the artificial intelligence agent. After appropriate training, the artificial intelligence agent provides an optimized field development plan instantly for new scenarios within the defined range of applicability. This approach has advantages over traditional optimization algorithms (e.g., particle swarm optimization, genetic algorithm) that are generally used to find a solution for a specific field development scenario and typically not generalizable to different scenarios. The performance of the artificial intelligence agents for two- and three-dimensional subsurface flow are compared to well-pattern agents. Optimization results using the new procedure are shown to significantly outperform those from the well pattern agents.


Sign in / Sign up

Export Citation Format

Share Document