Streamlining the Well Location Optimization Process - An Automated Approach Applied to a Large Onshore Carbonate Field

Abstract Infill drilling is a proved strategy to improve hydrocarbon recovery from reservoirs to increase production and maximize field value. Infill drilling projects address the following questions: 1) Where should the wells be drilled? 2) What should be their optimum trajectories? 3) What are the realistic ranges of incremental production of the infill wells? Answering these questions is important yet challenging as it requires the evaluation of multiple scenarios which is laborious and time intensive. This study presents an integrated workflow that allows the optimization of drilling locations using an automated approach that comprises cutting-edge optimization algorithms coupled to reservoir simulation. This workflow concurrently evaluates multiple scenarios until they are narrowed down to an optimum range according to pre-set objectives and honoring pre-established well design constraints. The simultaneous nature of the workflow makes it possible to differentiate between acceleration and real incremental recovery linked to proposed locations. In addition, the technology enables the optimization of all the elements that are relevant to the selection of drilling candidates, such as location, trajectory, inclination, and perforation interval. The well location optimization workflow was applied to a real carbonate large field; heavily faulted; with a well count of +400 active wells and subject to waterflooding. Hence the need for an automated way of finding new optimal drilling locations enabling testing of many locations. Also due to the significant full field model size; sector modelling capability was used such that the optimization, i.e. running many scenarios; could be carried out across smaller scale models within a reasonable time frame. Using powerful hardware and a fully parallelized simulation engine were also important elements in allowing the efficient evaluation of ranges of possible solutions while getting deeper insights into the field and wells responses. As a result of the study, 8 out of the original 9 well locations were moved to more optimal locations. The proposed optimized locations generate an incremental oil recovery increase of more than 70% compared to the original location (pre-optimization). In addition, the project was completed within 2 weeks of equivalent computational time which is a significant acceleration compared to a manual approach of running optimization on a full field model and it is significantly more straight forward than the conventional location selection process. The novelty of the project is introduced by customized python scripts. These scripts allow to achieve practical ways for placing the well locations to explore the solution space and at the same time, honor well design constraints, such as maximum well length, maximum step-out from the surface well-pad, and well perforation interval. Such in-built flexibility combined with automation and highly advanced optimization algorithms helped to achieve the project goals much easier and faster.

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Simulation Challenges In condensate Gas Full Field Model

10.29118/ipa.0.12.e.183 ◽

2018 ◽

Author(s):

Dong Chao

Keyword(s):

Field Model ◽

Full Field

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Incremental and acceleration production estimation and their effect on optimization of well infill locations in tight gas reservoirs

Journal of Petroleum Exploration and Production Technology ◽

10.1007/s13202-021-01179-1 ◽

2021 ◽

Author(s):

Atheer Dheyauldeen ◽

Omar Al-Fatlawi ◽

Md Mofazzal Hossain

Keyword(s):

Transient Flow ◽

Development Planning ◽

Main Role ◽

Gas Reservoirs ◽

Type Curves ◽

Field Development ◽

Drilling Performance ◽

Well Location ◽

Infill Drilling ◽

Tight Formations

AbstractThe main role of infill drilling is either adding incremental reserves to the already existing one by intersecting newly undrained (virgin) regions or accelerating the production from currently depleted areas. Accelerating reserves from increasing drainage in tight formations can be beneficial considering the time value of money and the cost of additional wells. However, the maximum benefit can be realized when infill wells produce mostly incremental recoveries (recoveries from virgin formations). Therefore, the prediction of incremental and accelerated recovery is crucial in field development planning as it helps in the optimization of infill wells with the assurance of long-term economic sustainability of the project. Several approaches are presented in literatures to determine incremental and acceleration recovery and areas for infill drilling. However, the majority of these methods require huge and expensive data; and very time-consuming simulation studies. In this study, two qualitative techniques are proposed for the estimation of incremental and accelerated recovery based upon readily available production data. In the first technique, acceleration and incremental recovery, and thus infill drilling, are predicted from the trend of the cumulative production (Gp) versus square root time function. This approach is more applicable for tight formations considering the long period of transient linear flow. The second technique is based on multi-well Blasingame type curves analysis. This technique appears to best be applied when the production of parent wells reaches the boundary dominated flow (BDF) region before the production start of the successive infill wells. These techniques are important in field development planning as the flow regimes in tight formations change gradually from transient flow (early times) to BDF (late times) as the production continues. Despite different approaches/methods, the field case studies demonstrate that the accurate framework for strategic well planning including prediction of optimum well location is very critical, especially for the realization of the commercial benefit (i.e., increasing and accelerating of reserve or assets) from infilled drilling campaign. Also, the proposed framework and findings of this study provide new insight into infilled drilling campaigns including the importance of better evaluation of infill drilling performance in tight formations, which eventually assist on informed decisions process regarding future development plans.

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Dynamic Selection of Realizations for Injection Well Location Optimization

10.3997/2214-4609.202011103 ◽

2020 ◽

Author(s):

R. Yusefzadeh ◽

M. Sharifi ◽

Y. Rafiei ◽

M. Ahmadi

Keyword(s):

Injection Well ◽

Dynamic Selection ◽

Location Optimization ◽

Well Location ◽

Selection Of

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An Efficient Bi-Objective Optimization Workflow Using the Distributed Quasi-Newton Method and Its Application to Well-Location Optimization

SPE Journal ◽

10.2118/203971-pa ◽

2021 ◽

pp. 1-17

Author(s):

Yixuan Wang ◽

Faruk Alpak ◽

Guohua Gao ◽

Chaohui Chen ◽

Jeroen Vink ◽

...

Keyword(s):

Optimization Problems ◽

Weighted Average ◽

Optimization Method ◽

Objective Functions ◽

Location Optimization ◽

Weighting Factors ◽

Well Location ◽

Derivative Free ◽

Nondominated Points ◽

Quasi Newton

Summary Although it is possible to apply traditional optimization algorithms to determine the Pareto front of a multiobjective optimization problem, the computational cost is extremely high when the objective function evaluation requires solving a complex reservoir simulation problem and optimization cannot benefit from adjoint-based gradients. This paper proposes a novel workflow to solve bi-objective optimization problems using the distributed quasi-Newton (DQN) method, which is a well-parallelized and derivative-free optimization (DFO) method. Numerical tests confirm that the DQN method performs efficiently and robustly. The efficiency of the DQN optimizer stems from a distributed computing mechanism that effectively shares the available information discovered in prior iterations. Rather than performing multiple quasi-Newton optimization tasks in isolation, simulation results are shared among distinct DQN optimization tasks or threads. In this paper, the DQN method is applied to the optimization of a weighted average of two objectives, using different weighting factors for different optimization threads. In each iteration, the DQN optimizer generates an ensemble of search points (or simulation cases) in parallel, and a set of nondominated points is updated accordingly. Different DQN optimization threads, which use the same set of simulation results but different weighting factors in their objective functions, converge to different optima of the weighted average objective function. The nondominated points found in the last iteration form a set of Pareto-optimal solutions. Robustness as well as efficiency of the DQN optimizer originates from reliance on a large, shared set of intermediate search points. On the one hand, this set of searching points is (much) smaller than the combined sets needed if all optimizations with different weighting factors would be executed separately; on the other hand, the size of this set produces a high fault tolerance, which means even if some simulations fail at a given iteration, the DQN method’s distributed-parallelinformation-sharing protocol is designed and implemented such that the optimization process can still proceed to the next iteration. The proposed DQN optimization method is first validated on synthetic examples with analytical objective functions. Then, it is tested on well-location optimization (WLO) problems by maximizing the oil production and minimizing the water production. Furthermore, the proposed method is benchmarked against a bi-objective implementation of the mesh adaptive direct search (MADS) method, and the numerical results reinforce the auspicious computational attributes of DQN observed for the test problems. To the best of our knowledge, this is the first time that a well-parallelized and derivative-free DQN optimization method has been developed and tested on bi-objective optimization problems. The methodology proposed can help improve efficiency and robustness in solving complicated bi-objective optimization problems by taking advantage of model-based search algorithms with an effective information-sharing mechanism. NOTE: This paper is published as part of the 2021 SPE Reservoir Simulation Conference Special Issue.

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Eye-centered visual receptive fields in the ventral intraparietal area

Journal of Neurophysiology ◽

10.1152/jn.00057.2014 ◽

2014 ◽

Vol 112 (2) ◽

pp. 353-361 ◽

Cited By ~ 18

Author(s):

Xiaodong Chen ◽

Gregory C. DeAngelis ◽

Dora E. Angelaki

Keyword(s):

Optic Flow ◽

Reference Frames ◽

Visual Stimulation ◽

Receptive Fields ◽

Large Field ◽

Visual Responses ◽

Full Field ◽

Spatial Reference Frames ◽

Visual Receptive Fields ◽

Ventral Intraparietal Area

The ventral intraparietal area (VIP) processes multisensory visual, vestibular, tactile, and auditory signals in diverse reference frames. We recently reported that visual heading signals in VIP are represented in an approximately eye-centered reference frame when measured using large-field optic flow stimuli. No VIP neuron was found to have head-centered visual heading tuning, and only a small proportion of cells had reference frames that were intermediate between eye- and head-centered. In contrast, previous studies using moving bar stimuli have reported that visual receptive fields (RFs) in VIP are head-centered for a substantial proportion of neurons. To examine whether these differences in previous findings might be due to the neuronal property examined (heading tuning vs. RF measurements) or the type of visual stimulus used (full-field optic flow vs. a single moving bar), we have quantitatively mapped visual RFs of VIP neurons using a large-field, multipatch, random-dot motion stimulus. By varying eye position relative to the head, we tested whether visual RFs in VIP are represented in head- or eye-centered reference frames. We found that the vast majority of VIP neurons have eye-centered RFs with only a single neuron classified as head-centered and a small minority classified as intermediate between eye- and head-centered. Our findings suggest that the spatial reference frames of visual responses in VIP may depend on the visual stimulation conditions used to measure RFs and might also be influenced by how attention is allocated during stimulus presentation.

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