Gas lift is one of the most widely used artificial lift methods, and the use of nodal analysis to generate the gas lift performance curve is well established. However, the optimal gas injection rate is often selected as the point with maximum liquid production, which neglects the cost of incremental injection gas volume. This paper investigates the determination of the optimal operational point using a multiobjective optimization technique by considering the trade-off between gas consumption and oil production. The indicator-based evolutionary algorithm transforms the multiobjective problem into a single objective one using the hypervolume metric computed in the objective space. For the gas lift problem, which is a bi-objective problem aimed at maximizing oil production while minimizing gas injection rate, the hypervolume metrics are identically equivalent to geometric hyperareas under the trade-off curve. The optimization is only applied to the monotonically increasing portion of the gas lift performance curve; thus, all trivial sub-optimal conditions are excluded. The optimal operational point of gas injection rate is determined by finding the maximum rectangular hyperarea under the performance curve. The proper determination of the optimal injection gas rate could not only improve the efficiency of the gas lift itself, but also reduce the burden on the maintenance of surface facilities. The method is also applied to the multi-well scenario where a novel multi-well gas lift performance curve is generated using multiobjective Genetic Algorithm, which could help determine the optimal gas allocation/distribution scenario. The described process is incorporated in an integrated workflow which further leads to fast delivery of analysis/results that enable production engineers to make smarter decisions faster in a repeatable way.
Liu, Guoxiang (Baker Hughes a GE Company) | Stephenson, Hayley (Baker Hughes a GE Company) | Shahkarami, Alireza (Baker Hughes a GE Company) | Murrell, Glen (Baker Hughes a GE Company) | Klenner, Robert (Energy & Environmental Research Center, University of North Dakota) | Iyer, Naresh (GE Global Research) | Barr, Brian (GE Global Research) | Virani, Nurali (GE Global Research)
Optimization problems, such as optimal well-spacing or completion design, can be resolved rapidly via surrogate proxy models, and these models can be built using either data-based or physics-based methods. Each approach has its strengths and weaknesses with respect to management of uncertainty, data quality or validation. This paper explores how data- and physics-based proxy models can be used together to create a workflow that combines the strengths of each approach and delivers an improved representation of the overall system. This paper presents use cases that display reduced simulation computational costs and/or reduced uncertainty in the outcomes of the models. A Bayesian calibration technique is used to improve predictability by combining numerical simulations with data regressions. Discrepancies between observations and surrogate outcomes are then observed to calibrate the model and improve the prediction quality and further reduce uncertainty. Furthermore, Gaussian Process Regression is used to locate global minima/maxima, with a minimal number of samples. To demonstrate the methodology, a reservoir model involving two wells in a drill space unit (DSU) in the Bakken Formation was constructed using publicly available data. This reservoir model was tuned by history matching the production data for the two wells. A data-based regression model was constructed based on machine learning technologies using the same dataset. Both models were coupled in a system to build a hybrid model to test the proposed process of data and physics coupling for completion optimization and uncertainty reduction. Subsequently, Gaussian Process Model was used to explore optimization scenarios outside of the data region of confidence and to exploit the hybrid model to further reduce uncertainty and prediction. Overall, both the computation time to identify optimal completion scenarios and uncertainty were reduced. This technique creates a robust framework to improve operational efficiency and drive completion optimization in an optimal timeframe. The hybrid modeling workflow has also been piloted in other applications such as completion design, well placement and optimization, parent-child well interference analysis, and well performance analysis.
Gupta, Anish (PETRONAS) | Narayanan, Puveneshwari (PETRONAS) | Trjangganung, Kukuh (PETRONAS) | Mohd Jeffry, Suzanna Juyanty (PETRONAS) | Tan, Boon Choon (PETRONAS) | Awang, M Rais Saufuan (PETRONAS) | Badawy, Khaled (PETRONAS) | Yip, Pui Mun (PETRONAS)
A matrix stimulation candidate screening workflow was developed with the objective to reduce the time and effort in identifying under-performing wells. The workflow was initially tested manually for few fields followed by inclusion in Integrated Operation for an automated screening of wells with suspected formation damage. Analysis done in three fields for stimulation candidate selection will be displayed with actual statistics.
The main aim of the work was to digitalize the selection of non-performing candidates rather than manually looking into performance of each well. A concept of Formation Damage Indicator (FDI) was combined with Heterogeneity Index (HI) of the formations to screen out the candidates. Separate database sets of Reservoir engineering, Petrophysicist and Production was integrated with suitable programming algorithms to come up with first set of screened wells evaluating well production performances, FDI and HI trends up to over the last 30 years. The shortlisted candidates were further screened on the basis of practical approach such as gas lift optimization, production trending, OWC-GOC contacts, well integrity and well history to come up with second round of screened candidates. The final candidates were analyzed further using nodal analysis models for skin evaluation and expected gain to come up with type of formation damage and expected remedial solution.
For fields A and D with a total of 210 strings each, the initial FDI and HI screening resulted in 70 and 120 strings being shortlisted, respectively. This was followed by a second round of screening with 25 and 35 strings being further shortlisted as stimulation candidates, respectively. Nodal analysis models indicated presence of high skin in 90% of the selected wells indicating a very good efficiency and function-test of the workflow. In addition to selection of the candidates, the identification of formation damage type was compiled on an asset-wise basis rather than field basis which helped in more efficient planning of remedial treatments using a multiple well campaign approach to optimize huge amount of cost. The entire screening process was done in one month which was earlier a herculean task of almost one year and much more man-hours. With effective manual testing of the workflow in two major fields, workflow was included in Integrated Operations for future automation to conduct the same task in minutes rather than months.
With this digitalized unique workflow, the selection of under-performing wells due to formation damage is now a one click exercise and a dynamic data. This workflow can be easily operated by any engineer to increase their operational efficiency for flow assurance issues saving tons of cost and time.
Agrawal, Nitesh (Cairn Oil & Gas, Vedanta Limited) | Chapman, Tom (Cairn Oil & Gas, Vedanta Limited) | Baid, Rahul (Cairn Oil & Gas, Vedanta Limited) | Singh, Ritesh Kumar (Cairn Oil & Gas, Vedanta Limited) | Shrivastava, Sahil (Cairn Oil & Gas, Vedanta Limited) | Kushwaha, Malay Kumar (Cairn Oil & Gas, Vedanta Limited) | Kolay, Jayabrata (Cairn Oil & Gas, Vedanta Limited) | Ghosh, Priyam (Cairn Oil & Gas, Vedanta Limited) | Das, Joyjit (Cairn Oil & Gas, Vedanta Limited) | Khare, Sameer (Cairn Oil & Gas, Vedanta Limited) | Kumar, Piyush (Cairn Oil & Gas, Vedanta Limited) | Aggarwal, Shubham (Cairn Oil & Gas, Vedanta Limited)
The objective of this paper is to present a suite of diagnostic methods and tools which have been developed to analyse and understand production performance degredation in wells lifted by ESPs in the Mangala field in Rajasthan, India. The Mangala field is one of the world’s largest full field polymer floods, currently injecting some 450kbbl/day of polymerized water, and a significant proportion of production is lifted with ESPs. With polymer breaking through to the producers, productivity and ESP performance in many wells have changed dramatically. We have observed rapidly reducing well productivity indexes (PI), changes to the pumps head/rate curve, increased inlet gas volume fraction (GVF) and reduction in the cooling efficiency of ESP motors from wellbore fluids. The main drivers for the work were to understand whether reduced well rates were a result of reduced PI or a degredation in the ESP pump curve, and whether these are purely down to polymer or combined with other factors, for example reduced reservoir pressure, increasing inlet gas, scale buildup, mechanical wear or pump recirculation.
The methodology adopted for diagnosis was broken in 5 parts – 1) Real time ESP parameter alarm system, 2) Time lapse analysis of production tubing pressure drop, 3) Time lapse analysis of pump head de-rating factor, 4) Time lapse analysis of pump and VFD horse power 5) Dead head and multi choke test data. With this workflow we were able to break down our understanding of production loss into its constituent components, namely well productivitiy, pump head/rate loss or additional tubing pressure drop. It was also possible to further make a data driven asseesment as to the most likely mechanisms leading to ESP head loss (and therefore rate loss), to be further broken own into whether this was due to polymer plugging, mechanical wear, gas volume fraction (GVF) de-rating, partial broken shaft/locked diffusers or holes/recirculation. In some cases a specific mechanism was compounded with an associated impact. For example, in ESPs equipped with an inlet screen, heavy polymer deposition over the screen was resulting in large pressure drops across the screen leading to lower head, but this also resulted in higher GVFs into first few stages of the pump, even though the GVF outside the pump were low, leading to further head loss from gas de-rating of the head curve. With knowledge of the magnitude of production losses from each of the underlying mechanisms, targeted remediation could then be planned.
The well and pump modelling adopted in the workflow utilise standard industry calculations, but the combination of these into highly integrated visual displays combined with time lapse analysis of operating performance, provide a unique solution not seen in commercial software we have screened.
The paper also provides various real field examples of ESP performance deterioration, showing the impact of polymer deposition leading to increased pump hydraulic friction losses, pump mechanical failure and high motor winding temperature. Diagnoses based on the presented workflow have in many cases been verified by inspection reports on failed ESPs. Diagnosis on ESPs that have not failed cannot be definitive, though the results of remediation (eg pump flush) can help to firm up the probable cause.
Sun, Zheng (MOE Key Laboratory of Petroleum Engineering and State Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum (Beijing)) | Shi, Juntai (MOE Key Laboratory of Petroleum Engineering and State Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum (Beijing)) | Wu, Keliu (MOE Key Laboratory of Petroleum Engineering and State Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum (Beijing)) | Zhang, Tao (MOE Key Laboratory of Petroleum Engineering and State Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum (Beijing)) | Feng, Dong (MOE Key Laboratory of Petroleum Engineering and State Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum (Beijing)) | Li, Xiangfang (MOE Key Laboratory of Petroleum Engineering and State Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum (Beijing))
Low-permeability coalbed-methane (CBM) reservoirs possess unique pressure-propagation behavior, which can be classified further as the expansion characteristics of the drainage area and the desorption area [i.e., a formation in which the pressure is lower than the initial formation pressure and critical-desorption pressure (CDP), respectively]. Inevitably, several fluid-flow mechanisms will coexist in realistic coal seams at a certain production time, which is closely related to dynamic pressure and saturation distribution. To the best of our knowledge, a production-prediction model for CBM wells considering pressure-propagation behavior is still lacking. The objective of this work is to perform extensive investigations into the effect of pressure-propagation behavior on the gas-production performance of CBM wells. First, the pressure-squared approach is used to describe the pressure profile in the desorption area, which has been clarified as an effective-approximation method. Also, the pressure/saturation relationship that was developed in our previous research is used; therefore, saturation distribution can be obtained. Second, an efficient iteration algorithm is established to predict gas-production performance by combining a new gas-phase-productivity equation and a material-balance equation. Finally, using the proposed prediction model, we shed light on the optimization method for production strategy regarding the entire production life of CBM wells. Results show that the decrease rate of bottomhole pressure (BHP) should be slow at the water single-phase-flow stage, fast at the early gas/water two-phase-flow stage, and slow at the late gas/water two-phase-flow stage, which is referred to as the slow/fast/slow (SFS) control method. Remarkably, in the SFS control method, the decrease rate of the BHP at each period can be quantified on the basis of the proposed prediction model. To examine the applicability of the proposed SFS method, it is applied to an actual CBM well in Hancheng Field, China, and it enhances the cumulative gas production by a factor of approximately 1.65.
Abeeb A. Awotunde, King Fahd University of Petroleum and Minerals Summary This paper evaluates the effectiveness of six dimension-reduction approaches. The approaches considered are the constant-control (Const) approach, the piecewise-constant (PWC) approach, the trigonometric approach, the Bessel-function (Bess) approach, the polynomial approach, and the data-decomposition approach. The approaches differ in their mode of operation, but they all reduce the number of parameters required in well-control optimization problems. Results show that the PWC approach performs better than other approaches on many problems, but yields widely fluctuating well controls over the field-development time frame. The trigonometric approach performed well on all the problems and yields controls that vary smoothly over time. Introduction Field-development optimization has continued to attract interest among researchers and end users of the technology.
A so-called perturb-and-observe (P&O) algorithm is adapted for a novel centrifugal pump to continuously optimize the point of operation. The novel pump coalesces and increases the size of oil droplets in the produced water, resulting in a unique relationship between the coalescing effect and the point of operation, and allowing for the successful implementation of the P&O algorithm. The algorithm was implemented in two different setups, one measuring the dropletsize distribution between the hydrocyclone and the pump, and the other measuring the oil concentration downstream of the hydrocyclone. The latter was considered the most robust because it required no prior knowledge of the system. Nonetheless, both setups achieved satisfying results and compared favorably with a third setup, where the optimal point of operation was predicted using measurements of the upstream produced-water characteristics. Introduction During oil and gas production, significant amounts of water are often produced along with the hydrocarbon mixture. Coproduced water, usually called produced water, can be a considerable source of pollution because it contains combinations of organic and inorganic materials that can lead to toxicity. Because of this, produced water is cleaned before being discharged into the sea or reinjected into a reservoir (Fakhru'l-Razi et al. 2009). Subsequently, in combination with other treatment technologies, hydrocyclones are often used to remove the remaining dispersed oil from the produced water.
In this paper we present our results, challenges and learnings, over a two-year period wherein robust multiobjective optimization was applied at the Mariner asset which is being currently developed. Many different problems were solved with different objectives. These problems were formulated based on the phases of planning and development at the asset. The optimization problems include drilling order and well trajectory optimization as the main objectives with reduction in water cut and reduction of gas production to minimize flaring as secondary objectives. We use the efficient stochastic gradient technique, StoSAG, to achieve optimization incorporating geological and petrophysical uncertainty. For some problems computational limitations introduced challenges while for other problems operational constraints introduced challenges for the optimization. Depending on the problems significant increases between 5% and 20% in the expected value of the objective function were achieved. For the multi-objective optimization cases we show that nontrivial optimal strategies are obtained which significantly reduce (40% decrease) gas production with minimal loss (less than 1%) in the economic objective. Our results illustrate the importance of flexible optimizations workflows to achieve results of significant practical value at different stages of the planning and development cycle at an operational asset.
In this study we explore the use of multilevel derivative-free optimization for history matching, with model properties described using PCA-based parameterization techniques. The parameterizations applied in this work are optimization-based PCA (O-PCA) and convolutional neural network-based PCA (CNN-PCA). The latter, which derives from recent developments in deep learning, is able to represent accurately models characterized by multipoint spatial statistics. Mesh adaptive direct search (MADS), a pattern search method that parallelizes naturally, is applied for the optimizations required to generate posterior (history matched) models. The use of PCA-based parameterization reduces considerably the number of variables that must be determined during history matching (since the dimension of the parameterization is much smaller than the number of grid blocks in the model), but the optimization problem can still be computationally demanding. The multilevel strategy introduced here addresses this issue by reducing the number of simulations that must be performed at each MADS iteration. Specifically, the PCA coefficients (which are the optimization variables after parameterization) are determined in groups, at multiple levels, rather than all at once. Numerical results are presented for 2D cases, involving channelized systems (with binary and bimodal permeability distributions) and a deltaic-fan system, using O-PCA and CNN-PCA parameterizations. O-PCA is effective when sufficient conditioning (hard) data are available, but it can lead to geomodels that are inconsistent with the training image when these data are scarce or nonexistent. CNN-PCA, by contrast, can provide accurate geomodels that contain realistic features even in the absence of hard data. History matching results demonstrate that substantial uncertainty reduction is achieved in all cases considered, and that the multilevel strategy is effective in reducing the number of simulations required. It is important to note that the parameterizations discussed here can be used with a wide range of history matching procedures (including ensemble methods), and that other derivative-free optimization methods can be readily applied within the multilevel framework.
Chemical enhanced oil recovery (EOR) methods have received increased attention in recent years since they have the ability to recover the capillary trapped oil. Successful chemical flooding application requires accurate numerical models and reliable forecast across multiple scales: core scale, pilot scale, and field scale. History matching and optimization are two key steps to achieve this goal.
For history matching chemical floods, we propose a general workflow for multi-stage model calibration using an Evolutionary Algorithm. A comprehensive chemical flooding simulator is used to model important physical mechanisms including phase behavior, cation exchange, chemical and polymer adsorption and capillary desaturation. First, we identify dominant reservoir and process parameters based on a sensitivity analysis. The history matching is then carried out in a stage-wise manner whereby the most dominant parameters are calibrated first and additional parameters are incorporated sequentially until a satisfactory data misfit is achieved. Next, a diverse subset of history matched models is selected for optimization using a Pareto-based multi-objective optimization approach. Based on the concept of dominance, Pareto optimal solutions are generated representing the trade-off between increasing oil recovery while improving the efficiency of chemical usage. These solutions are searched using a Non-dominated Sorting Genetic Algorithm (NSGA-II). Finally we implement a History Matching Quality Index (HMQI) with Moving Linear Regression Analysis to evaluate simulation results from history matching process. The HMQI provides normalized values for all objective functions having different magnitude and leads to a more consistent and robust approach to evaluate the updated models through model calibration.