Production optimization in Subang gas field using reservoir simulation

Author(s):  
A.D. Saputri
2018 ◽  
Vol 35 ◽  
pp. 01002
Author(s):  
Jerzy Stopa ◽  
Rafał Wiśniowski ◽  
Paweł Wojnarowski ◽  
Damian Janiga ◽  
Krzysztof Skrzypaszek

Accumulation and flow mechanisms in unconventional reservoir are different compared to conventional. This requires a special approach of field management with drilling and stimulation treatments as major factor for further production. Integrated approach of unconventional reservoir production optimization assumes coupling drilling project with full scale reservoir simulation for determine best well placement, well length, fracturing treatment design and mid-length distance between wells. Full scale reservoir simulation model emulate a part of polish shale – gas field. The aim of this paper is to establish influence of technical factor for gas production from shale gas field. Due to low reservoir permeability, stimulation treatment should be direct towards maximizing the hydraulic contact. On the basis of production scenarios, 15 stages hydraulic fracturing allows boost gas production over 1.5 times compared to 8 stages. Due to the possible interference of the wells, it is necessary to determine the distance between the horizontal parts of the wells trajectories. In order to determine the distance between the wells allowing to maximize recovery factor of resources in the stimulated zone, a numerical algorithm based on a dynamic model was developed and implemented. Numerical testing and comparative study show that the most favourable arrangement assumes a minimum allowable distance between the wells. This is related to the volume ratio of the drainage zone to the total volume of the stimulated zone.


2019 ◽  
Author(s):  
Azis Hidayat ◽  
Dwi Hudya Febrianto ◽  
Elisa Wijayanti ◽  
Diniko Nurhajj ◽  
Ahmad Sujai ◽  
...  

1994 ◽  
Author(s):  
J.K. Leong ◽  
J.R. Tenzer

2015 ◽  
Vol 50 (1) ◽  
pp. 29-38 ◽  
Author(s):  
MS Shah ◽  
HMZ Hossain

Decline curve analysis of well no KTL-04 from the Kailashtila gas field in northeastern Bangladesh has been examined to identify their natural gas production optimization. KTL-04 is one of the major gas producing well of Kailashtila gas field which producing 16.00 mmscfd. Conventional gas production methods depend on enormous computational efforts since production systems from reservoir to a gathering point. The overall performance of a gas production system is determined by flow rate which is involved with system or wellbore components, reservoir pressure, separator pressure and wellhead pressure. Nodal analysis technique is used to performed gas production optimization of the overall performance of the production system. F.A.S.T. Virtu Well™ analysis suggested that declining reservoir pressure 3346.8, 3299.5, 3285.6 and 3269.3 psi(a) while signifying wellhead pressure with no changing of tubing diameter and skin factor thus daily gas production capacity is optimized to 19.637, 24.198, 25.469, and 26.922 mmscfd, respectively.Bangladesh J. Sci. Ind. Res. 50(1), 29-38, 2015


SPE Journal ◽  
2018 ◽  
Vol 23 (06) ◽  
pp. 2409-2427 ◽  
Author(s):  
Zhenyu Guo ◽  
Albert C. Reynolds

Summary We design a new and general work flow for efficient estimation of the optimal well controls for the robust production-optimization problem using support-vector regression (SVR), where the cost function is the net present value (NPV). Given a set of simulation results, an SVR model is built as a proxy to approximate a reservoir-simulation model, and then the estimated optimal controls are found by maximizing NPV using the SVR proxy as the forward model. The gradient of the SVR model can be computed analytically so the steepest-ascent algorithm can easily and efficiently be applied to maximize NPV. Then, the well-control optimization is performed using an SVR model as the forward model with a steepest-ascent algorithm. To the best of our knowledge, this is the first SVR application to the optimal well-control problem. We provide insight and information on proper training of the SVR proxy for life-cycle production optimization. In particular, we develop and implement a new iterative-sampling-refinement algorithm that is designed specifically to promote the accuracy of the SVR model for robust production optimization. One key observation that is important for reservoir optimization is that SVR produces a high-fidelity model near an optimal point, but at points far away, we only need SVR to produce reasonable approximations of the predicting output from the reservoir-simulation model. Because running an SVR model is computationally more efficient than running a full-scale reservoir-simulation model, the large computational cost spent on multiple forward-reservoir-simulation runs for robust optimization is significantly reduced by applying the proposed method. We compare the performance of the proposed method using the SVR runs with the popular stochastic simplex approximate gradient (StoSAG) and reservoir-simulations runs for three synthetic examples, including one field-scale example. We also compare the optimization performance of our proposed method with that obtained from a linear-response-surface model and multiple SVR proxies that are built for each of the geological models.


SPE Journal ◽  
2019 ◽  
Vol 24 (04) ◽  
pp. 1526-1551
Author(s):  
Atefeh Jahandideh ◽  
Behnam Jafarpour

Summary Reservoir simulation is a valuable tool for performance prediction, production optimization, and field-development decision making. In recent years, significant progress has been made in developing automated workflows for optimization of production and field development by combining reservoir simulation with numerical optimization schemes. Although optimization under geologic uncertainty has received considerable attention, the uncertainty associated with future development activities has not yet been considered in field-development optimization. In practice, reservoirs undergo extensive development activities throughout their life cycle. Disregarding the possibility of future developments can lead to field-performance predictions and optimization results that might be far from optimal. This paper presents a stochastic optimization formulation to account for the uncertainty in future development activities while optimizing current decision variables (e.g., well controls and locations). A motivating example is presented first to demonstrate the significance of including the uncertainty in future drilling plans in oilfield-development optimization. Because future decisions might not be implemented as planned, a stochastic optimization framework is developed to incorporate future drilling activities as uncertain (random) variables. A multistage stochastic programming framework is introduced, in which the decision maker selects an optimal strategy for the current stage decisions while accounting for the uncertainty in future development activities. For optimization, a sequential approach is adopted whereby well locations and controls are repeatedly optimized until improvements in the objective function fall below a threshold. Case studies are presented to demonstrate the advantages of treating future field-development activities as uncertain events in the optimization of current decision variables. In developing real fields, where various unpredictable external factors can cast uncertainty regarding future drilling activities, the proposed approach provides solutions that are more robust and can hedge against changes/uncertainty in future development plans better than conventional workflows.


2012 ◽  
Author(s):  
Agha Hassan Akram ◽  
Arshad Majeed ◽  
Zaid Ashraf ◽  
Waqar A. Khan ◽  
Shah Abdur Rahman

2018 ◽  
Vol 10 (2) ◽  
pp. 65
Author(s):  
Arnaud Hoffmann

 This paper presents a model-based optimization solution suitable for short-term production optimization of large gas fields with wells producing into a common surface network into a shared gas treatment plant. The proposed methodology is applied to a field consisting of one dry gas reservoir with a CO2 content of 7.3% and one wet gas reservoir with a CO2 content of 2.8% and initial CGR of 15 stb/MMscf. 23 wells are producing, and all gas production is processed in a common gas treatment plant where condensates and CO2 are extracted from the reservoir gas. The final sales gas must honor compositional constraints (CO2 content and heating value). The proposed solution consists of a bi-level optimization algorithm. A Mixed Integer Linear Programming (MILP) formulation of the optimization problem is solved, assuming some key parameters in the gas plant to be constant. Hydraulic performances of the system, approximated using SOS2 piecewise linear models, and condensates and CO2 extraction, captured using simplified models, are included in the MILP. After solving the MILP, the values of the key parameters are calculated using a full simulation model of the gas plant and the new values are substituted in the MILP input data. This iterative procedure continues until convergence is achieved. Results show that the proposed methodology can find the optimum choke openings for all wells to maximize the total gas rate while honoring numerous surface constraints. The solution runs in 30 sec. and an average of 3-4 iterations is needed to achieve convergence. It is therefore a suitable solution for short-term production optimization and daily operations.


Author(s):  
Anita Theresa Panjaitan ◽  
Rachmat Sudibjo ◽  
Sri Fenny

<p>Y Field which located around 28 km south east of Jakarta was discovered in 1989. Three wells have been drilled and suspended. The initial gas ini place (IGIP) of the field is 40.53 BSCF. The field will be developed in 2011. In this study, reservoir simulation model was made to predict the optimum development strategy of the field. This model consisted of 1,575,064 grid cells which were built in a black oil simulator. Two field development scenarios were defined with and without compressor. Simulation results show that the Recovery Factor at thel end of the contract is 61.40% and 62.14% respectively for Scenarios I and II without compressor. When compressor is applied then Recovey Factor of Scenarios I and II is 68.78% and 74.58%, correspondingly. Based on the economic parameters, Scenario II with compressor is the most <br />attractive case, where IRR, POT, and NPV of the scenario are 41%, 2.9 years, and 14,808 MUS$.</p>


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