Improving Production Forecasting in a Mature Onshore Oilfield Brownfield in Southern Trinidad by Making Use of Software Modeling

2021 ◽  
Author(s):  
Simon Paul ◽  
Gerard Dukhoo ◽  
Murchison Phillip ◽  
Jediael Persadsingh

Abstract In Trinidad's mature onshore oilfields, operators have traditionally forecasted the initial production rates back calculated from decline models. These rates, then reduced annually by a predetermined decline model has been used to evaluate financial feasibility. This method does not make use of the reservoir pressure. This paper demonstrates how software modelling, utilizing the reservoir pressure can reasonably forecast the performance of low rate oil producers and alert the operator of the need for artificial lift from the inception of the production cycle. The objectives of the project were to determine remaining recoverable reserves, evaluate the potential for redevelopment (workovers and infill drilling) and to demonstrate that software modeling can be used to forecast production for an oil reservoir in a mature onshore oilfield in Southern Trinidad. Petroleum Experts Integrated Production Modeling (IPM) software suite was used for building all models. A comparison of the production forecasted by software modelling and the traditional method of forecasting initial production rates by back calculating from decline models was also undertaken. Using the available data and net oilsand maps, the fault block bulk volumes, oil in place and the remaining reserves were determined. These results were then used to identify fault blocks with potential workover well candidates and infill well locations. Research of well files and well logs were used in evaluating zones for potential recompletions, reperforation or perforation of additional footage for production. Forecasting and comparison of the initial production rates and ultimate cumulative production for the proposed infill wells and recompletions using the traditional IP/Decline model method and computer modeling was then performed. Form the data available, it was determined there were four blocks with remaining reserves that could be successfully recovered. The recovery methods proposed included the workover of two existing wells and drilling of two infill wells. Initial production rates and ultimate production volumes obtained by modeling of workover and new well performance had reasonably close agreement with those obtained by the traditional IP/Decline models. The results of the modeling, however indicated that all the wells required the use of pumping mechanisms (sucker rod/beam pumps) to sustain production over a ten-year period. The need for this important production mechanism would not have been realized from the IP/Decline method. An important distinction is that the modelling makes direct use of the reservoir pressure, whereas the IP/Decline model does not.

2021 ◽  
Author(s):  
Hassan Khan ◽  
Clifford Louis

Abstract Subsurface engineers pivot on surveillance of reservoir performance for future production rates and plan the optimization strategies at earliest. There are some techniques preferred for unconventional reservoirs such as numerical simulation and decline curve analysis (DCA) for production forecasting, but the uncertainty of uneconomical well test data often occurs in unconventional resources. Moreover, reservoir engineers can also hit a tailback in optimizing and tuning the model. Further, for DCA this approach is only appropriate for well/reservoir that are under boundary dominant flow regime, whereas fracture dominant flow regime is often observed for a longer period in unconventional hydraulically fractured reservoirs. Therefore, to resolve this issue, oil & gas industry (O&G) can adopt AI (Artificial Intelligence) based Algorithms for production forecasting. This paper presents a data-driven algorithm, known as Artificial Neural Networks (ANNs), along with time series forecasting that is a well-known statistical technique. Machine learning model trained by a past well performance data such as tubing head pressure (THP), flowing bottom-hole pressure can predict future production rates. This can be an efficient technique for subsurface engineers to monitor and optimize well performance. Time series neural networks were used for training the model at top and bottom node of the well with variating pressures in the past. After training and validation, the model predicted a target parameter that was gas rate. ANNs are inspired by biological neurons that are present in human brain, a powerful computing tool to make decisions after fueling itself with data. Moreover, prediction (t+1) nonlinear automated regression is preferred for accurate step ahead. Production rates and constraints of unconventional reservoirs were used to train the model. In our results, the NN based model gave the co-efficient of determination (R2) of 0.996 that shows nearly an exact precision. Furthermore, the values generated from NN Model and Arp's decline curve calculations were plotted for validation and it turned out that ANN can accurately predict the parameters. The Neural Network model is a novel approach for production forecasting, of unconventional reservoirs and help engineers in corporate decision making. This approach can mitigate the need of uneconomical well test operations and further provide confidence to production engineers in terms of data and result expectations.


2021 ◽  
Author(s):  
Mohammed Ahmed Al-Janabi ◽  
Omar F. Al-Fatlawi ◽  
Dhifaf J. Sadiq ◽  
Haider Abdulmuhsin Mahmood ◽  
Mustafa Alaulddin Al-Juboori

Abstract Artificial lift techniques are a highly effective solution to aid the deterioration of the production especially for mature oil fields, gas lift is one of the oldest and most applied artificial lift methods especially for large oil fields, the gas that is required for injection is quite scarce and expensive resource, optimally allocating the injection rate in each well is a high importance task and not easily applicable. Conventional methods faced some major problems in solving this problem in a network with large number of wells, multi-constrains, multi-objectives, and limited amount of gas. This paper focuses on utilizing the Genetic Algorithm (GA) as a gas lift optimization algorithm to tackle the challenging task of optimally allocating the gas lift injection rate through numerical modeling and simulation studies to maximize the oil production of a Middle Eastern oil field with 20 production wells with limited amount of gas to be injected. The key objective of this study is to assess the performance of the wells of the field after applying gas lift as an artificial lift method and applying the genetic algorithm as an optimization algorithm while comparing the results of the network to the case of artificially lifted wells by utilizing ESP pumps to the network and to have a more accurate view on the practicability of applying the gas lift optimization technique. The comparison is based on different measures and sensitivity studies, reservoir pressure, and water cut sensitivity analysis are applied to allow the assessment of the performance of the wells in the network throughout the life of the field. To have a full and insight view an economic study and comparison was applied in this study to estimate the benefits of applying the gas lift method and the GA optimization technique while comparing the results to the case of the ESP pumps and the case of naturally flowing wells. The gas lift technique proved to have the ability to enhance the production of the oil field and the optimization process showed quite an enhancement in the task of maximizing the oil production rate while using the same amount of gas to be injected in the each well, the sensitivity analysis showed that the gas lift method is comparable to the other artificial lift method and it have an upper hand in handling the reservoir pressure reduction, and economically CAPEX of the gas lift were calculated to be able to assess the time to reach a profitable income by comparing the results of OPEX of gas lift the technique showed a profitable income higher than the cases of naturally flowing wells and the ESP pumps lifted wells. Additionally, the paper illustrated the genetic algorithm (GA) optimization model in a way that allowed it to be followed as a guide for the task of optimizing the gas injection rate for a network with a large number of wells and limited amount of gas to be injected.


2010 ◽  
Author(s):  
Vai Yee Hon ◽  
Suzalina Zainal ◽  
Ismail Mohd Saaid

2021 ◽  
Author(s):  
Nasser AlAskari ◽  
Muhamad Zaki ◽  
Ahmed AlJanahi ◽  
Hamed AlGhadhban ◽  
Eyad Ali ◽  
...  

Abstract Objectives/Scope: The Magwa and Ostracod formations are tight and highly fractured carbonate reservoirs. At shallow depth (1600-1800 ft) and low stresses, wide, long and conductive propped fracture has proven to be the most effective stimulation technique for production enhancement. However, optimizing flow of the medium viscosity oil (17-27 API gravity) was a challenge both at initial phase (fracture fluid recovery and proppant flowback risks) and long-term (depletion, increasing water cut, emulsion tendency). Methods, Procedures, Process: Historically, due to shallow depth, low reservoir pressure and low GOR, the optimum artificial lift method for the wells completed in the Magwa and Ostracod reservoirs was always sucker-rod pumps (SRP) with more than 300 wells completed to date. In 2019 a pilot re-development project was initiated to unlock reservoir potential and enhance productivity by introducing a massive high-volume propped fracturing stimulation that increased production rates by several folds. Consequently, initial production rates and drawdown had to be modelled to ensure proppant pack stability. Long-term artificial lift (AL) design was optimized using developed workflow based on reservoir modelling, available post-fracturing well testing data and production history match. Results, Observations, Conclusions: Initial production results, in 16 vertical and slanted wells, were encouraging with an average 90 days production 4 to 8 times higher than of existing wells. However, the initial high gas volume and pressure is not favourable for SRP. In order to manage this, flexible AL approach was taken. Gas lift was preferred in the beginning and once the production falls below pre-defined PI and GOR, a conversion to SRP was done. Gas lift proved advantageous in handling solids such as residual proppant and in making sure that the well is free of solids before installing the pump. Continuous gas lift regime adjustments were taken to maximize drawdown. Periodical FBHP surveys were performed to calibrate the single well model for nodal analysis. However, there limitations were present in terms of maximizing the drawdown on one side and the high potential of forming GL induced emulsion on the other side. Horizontal wells with multi-stage fracturing are common field development method for such tight formations. However, in geological conditions of shallow and low temperature environment it represented a significant challenge to achieve fast and sufficient fracture fluid recovery by volume from multiple fractures without deteriorating the proppant pack stability. This paper outlines local solutions and a tailored workflow that were taken to optimize the production performance and give the brown field a second chance. Novel/Additive Information: Overcoming the different production challenges through AL is one of the keys to unlock the reservoir potential for full field re-development. The Magwa and Ostracod formations are unique for stimulation applications for shallow depth and range of reservoirs and fracture related uncertainties. An agile and flexible approach to AL allowed achieving the full technical potential of the wells and converted the project to a field development phase. The lessons learnt and resulting workflow demonstrate significant value in growing AL projects in tight and shallow formations globally.


Energies ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 1525
Author(s):  
Ruud Weijermars ◽  
Kiran Nandlal

This paper advances a practical tool for production forecasting, using a 2-segment Decline Curve Analysis (DCA) method, based on an analytical flow-cell model for multi-stage fractured shale wells. The flow-cell model uses a type well and can forecast the production rate and estimated ultimate recovery (EUR) of newly planned wells, accounting for changes in completion design (fracture spacing, height, half-length), total well length, and well spacing. The basic equations for the flow-cell model have been derived in two earlier papers, the first one dedicated to well forecasts with fracture down-spacing, the second one to well performance forecasts when inter-well spacing changes (and for wells drilled at different times, to account for parent-child well interaction). The present paper provides a practical workflow, introduces correction parameters to account for acreage quality and fracture treatment quality. Further adjustments to the flow-cell model based 2-segment DCA method are made after history matching field data and numerical reservoir simulations, which indicate that terminal decline is not exponential (b = 0) but hyperbolic (with 0 < b< 1). The timing for the onset of boundary dominated flow was also better constrained, using inputs from a reservoir simulator. The new 2-segment DCA method is applied to real field data from the Eagle Ford Formation. Among the major insights of our analyses are: (1) fracture down-spacing does not increase the long-term EUR, and (2) fracture down-spacing of real wells does not result in the rate increases predicted by either the flow-cell model based 2-segment DCA (or its matching reservoir simulations) with the assumed perfect fractures in the down-spaced well models. Our conclusion is that real wells with down-spaced fracture clusters, involving up to 5000 perforations, are unlikely to develop successful hydraulic fractures from each cluster. The fracture treatment quality factor (TQF) or failure rate (1-TQF) can be estimated by comparing the actual well performance with the well forecast based on the ideal well model (albeit flow-cell model or reservoir model, both history-matched on the type curve).


2014 ◽  
Vol 17 (02) ◽  
pp. 209-219 ◽  
Author(s):  
H.. Luo ◽  
G.F.. F. Mahiya ◽  
S.. Pannett ◽  
P.. Benham

Summary The evaluation of expected ultimate recovery (EUR) for tight gas wells has generally relied upon the Arps equation for decline-curve analysis (DCA) as a popular approach. However, it is typical in tight gas reservoirs to have limited production history that has yet to reach boundary-dominated flow because of the low permeability of such systems. Commingled production makes the situation even more complicated with multiboundary behavior. When suitable analogs are not available, rate-transient analysis (RTA) can play an important role to justify DCA assumptions for production forecasting. The Deep-basin East field has been developed with hydraulically fractured vertical wells through commingled production from multiple formations since 2002. To evaluate potential of this field, DCA type curves for various areas were established according to well performance and geological trending. Multiple-segment DCA methodology demonstrated reasonable forecasts, in which one Arps equation is used to describe the rapidly decreasing transient period in early time and another equation is used for boundary-dominated flow. However, a limitation of this approach is the uncertainty of the forecast in the absence of extended production data because the EUR can be sensitive to adjustments in some assumed DCA parameters of the second segment. In this paper, we used RTA to assess reservoir and fracture properties in multiple layers and built RTA-type well models around which uncertainty analyses were performed. The distributions of the model properties were then used in Monte Carlo analysis to forecast production and define uncertainty ranges for EUR and DCA parameters. The resulting forecasts and EUR distribution from RTA modeling generally support the DCA assumptions used for the type curves for corresponding areas of the field. The study also showed how the contribution from the various commingled layers changes with time. The proposed workflow provides a fit-for-purpose way to quantify uncertainties in tight gas production forecasting, especially for cases when production history is limited and field-level numerical simulation is not practicable.


Author(s):  
D. I. Sidorkin ◽  
K. S. Kupavykh

The paper analyzes the main techniques and technologies of oil fluid recovery in the context of energy consumption, significantly rising over the latest decade. It is recognized that the number of publications in the area of energy efficiency is growing steadily. Currently Russian oil and gas industry are facing the task of accelerating reduction of energy consumption while preserving, or even increasing, production rates. The task is complicated by the fact that the majority of deposits in Russia either have already entered (primarily, Volga-Ural region) or are now entering (West Siberia) their last stage of exploration, whereas new deposits in East Siberia are only being brought into production. Furthermore, a lot of new deposits, which provide for high recovery rates, are profitable a priori as at the first stage of exploration they do not need any artificial lift due to their free flow production without any oil well pumps. However, there is a significant share of new deposits with low-permeability reservoirs, which require either a system of reservoir pressure maintenance or periodic hydraulic fracturing. At the same time deposits at the late stages of exploration, apart from the use of pump units, systems of reservoir pressure maintenance and hydraulic fracturing, require regular repair and restoration, measures against salt and heavy oil sediments, mechanical impurities, flooding, etc., which all has a negative effect on well profitability. In order to solve these problems, the authors review existing methods and calculate specific energy consumption using various pump systems for hypothetical wells, varying in yield. According to the research results, it has been revealed that from the point of view of energy efficiency, it is desirable to equip low- and low-yield wells with sucker rod progressive cavity pump units, medium-yield ones – with electric progressive cavity pumps driven by permanent magnet motor, medium- and high-yield wells – with electric progressive cavity pumps or electric submersible pumps driven by permanent magnet motor, depending on the characteristics of the pumpedout oil fluid.


2010 ◽  
Vol 50 (2) ◽  
pp. 719
Author(s):  
Nathan Rayner ◽  
Ross Hendrie ◽  
Michael Bowe

An assessment framework for selecting optimal drilling and completion, forecasting and operating procedures in low permeability coals. Themes covered: low permeability coal drilling and production alternatives; production forecasting and operating procedures; and, economics and technical feasibilty. Arrow Energy Limited has been exploring for and producing from moderate to low permeability coals for over eight years, both in Australia and internationally. Arrow has developed a systematic approach to assessing the optimal drilling and completion, forecasting and operating procedures to evaluate the best appraisal and development options. Following the confirmation that the coal resource is of low permeability (less than 5mD) the selection of the drilling and completion strategies is inter-linked with the expected production and operating procedures. This paper summarises the approach taken by each of these discipline areas and maps out the key alternatives, data requirements and selection criteria used to recommend a production pilot in a low permeability environment. The first stage of the assessment is to review the drilling alternatives. This includes consideration of horizontal versus deviated or vertical wells, wellbore stability and solids production conditions, stimulation requirements and the production string, including artificial lift. Production forecasting is conducted with due regard to the technical alternatives screened as part of the drilling assessment. The quality of the forecast will be determined by the available data and the use of the appropriate forecasting tools ranging from analogue assessments, simple single well modelling through to 3D reservoir modelling. Finally, the production procedures appropriate for the well will be selected based on the well configuration and forecast deliverability as well as regional geology and geomechanics. This framework ensures that a consistent methodology is applied for selecting well type and operations that maximises the flow potential from our low permeability coals while ensuring due consideration to economics and technical feasibility.


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