Rapid Scenario and Risk Analysis for a Complex Gas Field with Large Uncertainties

Hegstad, Bjørn Kåre; Tollefsen, Svein; Arghir, D.V.; Cullick, A.S.; Narayanan, Keshav; Heath, D.E.; Lever, J.C.

doi:10.2118/90961-ms

Cited by 11 publications

(5 citation statements)

References 14 publications

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“…These estimations can in turn be derived, for example, from reservoir analogs of the reservoir of interest (Hodgin and Harrell 2006;Sharma et al 2010;Sidle and Lee 2010). If the plateau duration is known (e.g., it may be given by some business unit and/or derived from analysis of existing oil demand and facilities associated with that demand), we solve a (relatively simple) optimization problem with the FOPR that corresponds to the production plateau as the only variable, and a measure of discrepancy regarding the specified plateau duration as the objective function.…”

Section: (Bottom Right)mentioning

confidence: 99%

Opening New Opportunities With Fast Reservoir-Performance Evaluation Under Uncertainty: Brugge Field Case Study

Torrado

Echeverría-Ciaurri²,

Mello³

et al. 2015

SPE Economics &Amp; Management

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Decision making under uncertainty can be quite challenging, especially when complex numerical simulations are considered in the work flow and the decision has to be made relatively fast (e.g., in hours). This is the case when one needs to rank a given field portfolio within a limited budget and with acquisition constraints. If the ranking measure associated with each field is properly and rapidly evaluated, new prospect opportunities, which may lead to a favorable strategic position, can be readily identified.In this paper, we propose an efficient methodology for computing a "production-potential" measure that can be used to rank greenfield portfolios in the presence of geological uncertainty, quantifying both uncertainty and risk propagation. Next, we briefly describe the basics of the method proposed. First, uncertainty in sedimentary variability and flow behavior has to be characterized by a number of representative geological realizations. Sampling techniques are used to significantly reduce the number of realizations while preserving accuracy in the description and uncertainty propagation. Thereafter, multiple and varied field-development plans, based on primary/secondary-recovery mechanisms, are automatically generated while accounting for key parameters related to the number, drilling locations, and drilling sequence of wells. In these plans the reservoir is clustered by areas with similar production/injection potential, and the well locations and drilling schedules are obtained accordingly. The well controls are determined through estimations of the fieldrecovery factor. By means of experimental-design techniques a relatively small number of field-development plans are selected to capture the most significant production profiles. Each of these development plans is simulated for the realizations sampled previously, and the production-potential measure [e.g., average net present value (NPV) over all sampled realizations] is computed for all the plans. The highest of these measures (i.e., the best development plan) can be used for ranking the greenfield in the portfolio. Response-surface procedures are considered to perform additional analysis computations within iterative optimization procedures. It is important to note that other statistics related to the exploitation potential (e.g., standard deviation of the NPV) can also be used to complement the ranking, thereby mitigating the decision makers' risk tolerance. The methodology has been tested on the Brugge Field benchmark, which presents 104 realizations of multiple geological parameters. The benchmark has been modified to simulate a greenfield scenario. The ranking measure is the (discounted) NPV averaged over the 104 realizations. The proposed work flow yields a ranking measure of USD 5.43 billion, and the computational cost is approximately 1,900 simulations (performed in a parallel-computing environment). This NPV is somewhat higher than those found for the Brugge benchmark (with similar modified settings) by other researchers. To validate the result...

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Section: (Bottom Right)mentioning

confidence: 99%

Opening New Opportunities With Fast Reservoir-Performance Evaluation Under Uncertainty: Brugge Field Case Study

Torrado

Echeverría-Ciaurri²,

Mello³

et al. 2015

SPE Economics &Amp; Management

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“…The main points discussed in classroom were: 4,5,6,7,8 . A computer software for Monte Carlo simulation of simple problems was distributed.…”

Section: Course Syllabusmentioning

confidence: 99%

Importance of Economic and Risk Analysis on Today's Petroleum Engineering Education

Cunha¹

2007

Proceedings of SPE Annual Technical Conference and Exhibition

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fax 01-972-952-9435. AbstractGraduate students in a master or PhD program tend to naturally concentrate their efforts on the understanding of theoretical issues related to their main research area of interest. This will frequently lead to a cultural shock when they, after concluding their program, enter or return to the job market.Commonly these professionals are asked to analyze, develop and/or implement projects based not only on technical premises but also on solid and very well supported economical feasibility studies. Frequently the common graduate academic background does not prepare students for those tasks.The importance of a thorough understanding of economic issues and associated risks is even more noticeable in the oil industry, where uncertainties related to oil and gas reserves, prices and government regulations make any long term project extremely risky.Based on the abovementioned factors, a graduate course was designed and implemented in order to prepare the students to deal with the main economic issues and challenges faced by the industry. Besides the basics on economic engineering, the course covers advanced material related to budgeting, scheduling and corporate planning.Monte Carlo simulation, economic decision tools, risk analysis for oil industry projects, investment risk and simulation, economic analysis of operations, production forecasts and its associated costs and expected profits are also studied.Furthermore, the students are also required to prepare a project where actual challenges from the industry are analyzed under the perspective of the risks and uncertainties involved. In the project, associated costs and economic results also have to be determined and scrutinized. All projects are presented in a seminar at the end of the course. In addition, a "Newsletter" analyzing current problems, challenges and industry development is produced weekly by the group during the duration of the course.These two tools, the project and the Newsletter, have raised in participants a great deal of interest for economic matters related to each one's individual area of expertise.The paper details the course experience providing examples of main projects developed as well as participants' feedback.

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“…The literature in Field Development Plan (FDP) optimization under uncertainty is quite profuse and mainly based on decision trees, data assimilation frameworks and optimization algorithms. Worth citing the work on asset evaluation and decision making from (Hegstad et al 2004), (Evans 2005), (Saputelli et al 2007), and in terms of optimization under uncertainty the previous work of (De Paola et al, 2018) and (Rodriguez-Torrado et al 2015).…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning For Field Development Policy Optimization

Paola

Ibanez-Llano

Ríos³

et al. 2020

Day 4 Thu, October 29, 2020

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What is the next best drilling decision to make in a Field Development Plan (FDP)? This is the key question we address in this work. A FDP consists of a sequence of decisions. Each action we take affects the reservoir and conditions any future decision. The novelty of our proposed approach is the consideration of the sequential nature of the decisions through the framework of Dynamic Programming (DP) and Reinforcement Learning (RL). In this framework, each scheduled drilling decision depends on the observations acquired between drillings. This methodology allows moving the focus from a static Field Development Plan optimization to a more dynamic framework that we call Field Development Policy Optimization. In addition to the formulation of this new framework, we have applied this methodology to optimize the development of a real oil and gas field. We model the FDP optimization problem under subsurface uncertainty as a Partially Observable Markov Decision Process (POMDP) and solve it through a RL algorithm in order to find an optimal drilling policy. Our methodology works for a general reservoir with a given set of geological model representing. To speed up the learning process we utilize a trained Deep Recurrent Neural Network (RNN) to approximate the reservoir simulator flows, which are subsequently used to compute the economic performance of a drilling policy through its discounted cash flows. The RNN is trained and tested on a set of reservoir simulator runs over randomly sampled realizations of our reservoir model, well location, type and control sequences of a drilling plan. From all the possible decisions involved in a FDP, we focus here only on finding optimal adaptive well drilling schedules (locations of vertical wells and well types). The RL agent learns the best drilling schedule policy by generating simulated episodes of experience and iteratively improving the policy using a Q value function approximated by a neural network trained across episodes. The final solution consists of an adaptive Field Development Plan yielding the highest expected Net Present Value (NPV), computed within a given, available time budget. It specifies an adaptive drilling schedule of producer and injector, well locations and well controls, as a function of the information obtained at each drilling step. The methodology has been applied to an actual reservoir for infill well location decisions. In this case, our objective is finding the best well placement and well type for the next producer and injector wells, as well as the optimization of the control schedule for new and preexisting wells in the reservoir. Our results show the learning progress of our RL algorithm until finding the optimal drilling plan. The robustness of the solution is evaluated across the best-trained policies. Methodology and results have been validated using a brute force sampling approach. Both RL and brute force approaches were possible due to our fast-to-compute RNN approximation of the reservoir simulator. This work represents, to our knowledge, the first application of an end-to-end AI workflow for Field Development Policy Evaluation in real fields, based on Reinforcement Learning and Deep Learning. The proposed methodology puts together an optimal field evaluation in planning and a surveillance workflow for a reactive decision-making.

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Rapid Scenario and Risk Analysis for a Complex Gas Field with Large Uncertainties

Cited by 11 publications

References 14 publications

Opening New Opportunities With Fast Reservoir-Performance Evaluation Under Uncertainty: Brugge Field Case Study

Opening New Opportunities With Fast Reservoir-Performance Evaluation Under Uncertainty: Brugge Field Case Study

Importance of Economic and Risk Analysis on Today's Petroleum Engineering Education

Reinforcement Learning For Field Development Policy Optimization

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