Baruah, Nabajit (Oil & Natural Gas Corporation) | Mandal, Dipak (Oil & Natural Gas Corporation) | Jena, Smita Swarupa (Oil & Natural Gas Corporation) | Sahu, Sunil Kumar (Oil & Natural Gas Corporation)
This paper examines the prospect of Gas Assisted Gravity Drainage (GAGD) process in improving recovery from a sandstone reservoir by injecting produced gas back into the crestal part of the reservoir. Besides recovery improvement, immiscible gas injection ensures near Zero Flaring strategy. The process has been found to be ideal in reservoirs with high permeability and reasonable dip to maximize oil production wherever a sufficient gas source exists. Based on the study, gas injection is recommended at the crestal part of the reservoir under study at the rate equivalent to the produced gas to maintain pressure, arrest gas cap shrinkage and improve recovery.
A reservoir with bottom water drive mechanism has a high tendency to generate water coning effect in their production life. As a result of water coning phenomenon, the well has a low critical safe rate which limits the productivity of the reservoir. Consequently, a new innovation for completion design in an oil well with a bottom aquifer drive is needed. The author offers a Downhole Water Sink (DWS) system to solve this problem.
DWS is a dual completion design innovation where two tubing strings are installed into the well to produce both water and oil simultaneously by different tubing. The main principle of DWS is to create a stable pressure drawdown in oil and water zone so that a stable oil-water contact is formed. DWS application in a multilayered reservoir expected to be able to resolve the water coning phenomenon thus the recovery factor increased and the well becomes economic to be produced. In this paper, the study approach involved by numerical simulation within IMPES methodology (Implicit Pressure Explicit Saturation) and Thomas’s algorithm to solve iteration. Completion modeling is creating two wells on the similar coordinate in several layered reservoirs aims to produce oil and water separately on tubing on the well.
The percentage of water cut on oil production tubing is 0% while the percentage of water cut on water production tubing is 100%. This thing shows that DWS completion system will give a greater cumulative oil production in a high production rate and the oil is oil-free water. It is observed that the successful implementation of DWS in a multilayered reservoir is taken place. The well with DWS design configuration for the WDP system shows a better performance of oil productivity compares to a conventional well completion design. This result is supported by no water production observed at oil production tubing on the surface well level. There are some parameters that affect DWS system application modeling i.e. mobility ratio, vertical and absolute horizontal permeability (kv & kh) also perforation interval.
Down-Hole Water Sink is an appropriate innovation to eliminate water coning and producing oil with high recovery factor. DWS application in a multilayered reservoir with bottom aquifer driving mechanism shows a better performance of oil productivity compares to a conventional well completion design. This result is supported by no water production observed at oil production tubing on the surface well level.
Gupta, M K (Oil and Natural Gas Corporation Ltd.) | Sukanandan, J N (Oil and Natural Gas Corporation Ltd.) | Singh, V K (Oil and Natural Gas Corporation Ltd.) | Bansal, R (Oil and Natural Gas Corporation Ltd.) | Pawar, A S (Oil and Natural Gas Corporation Ltd.) | Deuri, Budhin (Oil and Natural Gas Corporation Ltd.)
This paper discusses a case study of one of the onshore field of ONGC where while processing well fluid, frequent surge has been observed leading to shutdown of the SDVs creating severe operational problems and loss of production. It was imperative to find out the problematic wells/lines located in clusters which contribute for surge formation and mitigation approach with minimum modifications.
A transient complex network of sixty five wells flowing with a different lift mode such as intermittent gas lift, continuous gas lift etc were developed in a dynamic multiphase flow simulator OLGA. Time cycle of each well were introduced for intermittent lift wells. Simulation study reveals pulsating transient trends of liquid flow, pressure which was matched with the real time data of the plant and hence confirms the accuracy of the model. After verifying the results, different scenarios were created to determine the causes of surge formation. After finding the cause, a low cost approach was considered for surge mitigations.
An integrated rigorous simulation was carried out in OLGA, by feeding more than 12,000 data points to obtain model match. Several scenarios were also created such as optimization of lift gas quantity, optimization of elevation and size. Trend obtained after each scenario was pulsating behaviour and it matched with the real time data appearing in the SCADA system of the field. After rigorous simulation with each scenario, it was established that the cause of surge forming wells/pipelines. Once the root cause of surge has been confirmed then quantum of liquid generated due to surge was determined. Adequacy checks of the existing separators were carried out to estimate the handling capacity of the existing separators at prevalent operating condition. After adequacy check it was found that existing separators cannot handle the surge generated in that time interval leading to cross the high-high safety level, resulting closure of shut down valve (SDV). After establishment of root cause of the surge, a low cost solution with small modification in pipelines and control system/valves was adopted to arrest the surges. It was first of its kind simulation carried out for a huge network of wells/ pipelines by feeding more than 12,000 data to analyze the surge formation cause and capture its dynamism owing to wide array of suspected causes. This will help to address the challenges of efficiently reviewing the entire pipeline network while designing new well pad/GGS and will also help to arrest surge by adopting a low cost solution wherever such situation arises.
The paper presents a case of applying classical reservoir engineering technique of material balance to one of the major carbonate reservoir in the western offshore basin in India that eventually led to establishment of more hydrocarbon volumes.
During Material Balance calculation, multiple runs were performed to match the pressure performance with a balance between the aquifer strength and hydrocarbon volume that was in agreement with geological understanding and performance of the field. The analysis indicated extra energy support that may be in the form of aquifer or higher in-place volumes. Following the in-house developed SIMEX (Simultaneous Exploration) approach a vertical well was identified for testing below assumed lowest known oil (LKO) limit.
The material balance study formed the basis for revisiting the geological understanding. The establishment of oil through the testing of well necessitated the revision of geological maps and re-estimation of hydrocarbon in-place volumes. Accordingly property maps have been prepared and volumes are revised. The revised volumes are about 14% more than the previous estimation. Similar approach was successfully applied to another reservoir in the Mumbai High field. Presence of more established oil will help in planning future strategies for field development.
Especially in fields where enough pressure production history is available, it is important to reassess the field's potential from time to time through simple and classical techniques available. Fields with multiple reservoirs have added advantage of developing the established hydrocarbons through zone transfer and in turn saving significant cost of drilling new well. This being a proven and classical technique, can be applied to other analogous reservoirs.
Yang, Junjie (Baker Hughes, a GE Company) | Karam, Pierre (Baker Hughes, a GE Company) | Cozyris, Kristian (Baker Hughes, a GE Company) | Hustak, Crystal (Baker Hughes, a GE Company) | Doherty, James (Riley Exploration – Permian, LLC) | Allen, Carmen (Riley Exploration – Permian, LLC)
As a well-known tight oil dolomite reservoir in Texas, San Andres formation has attracted broad attention about horizontal drilling and development strategy. To optimize the oil recovery and asset’s economics, the aim of the study was to use an integrated approach to understand reservoir heterogeneity and performance, determine optimal landing zone and its impact on production, understand fracture geometry using different pumping schedules, and the optimal cluster spacing. In addition, the potential benefit of a refrac and infill drilling program was also investigated.
To tackle the optimization problem, an integrated reservoir modeling workflow was developed. Starting with a 1-D geomechanical model which captures the in situ stress profile and rock mechanics, hydraulic fracture modeling was developed to history match the treatment process, and therefore a comprehensive fracture geometry can be estimated. In the interim, a geological model with populated reservoir properties was established based on the offset data including petrophysical logs, imaging logs and cores. After calibration, the dynamic reservoir model was built to test multiple sensitivity runs for an optimized field development strategy.
Geological modeling separated the field into two models to study the variation of properties on the east and west side. The east section shows a higher porosity and lower saturations. Those water saturations increase below the main pay zone indicating a potential water source. In addition, special core analysis shows a strong oil-wet nature of the reservoir rock. In the east section, sensitivity runs included infill development and variations in landing depth. It is noted that the production is not sensitive to landing zone because fracture geometry is primarily controlled by vertical stress profile. In the west section, sensitivity runs included refrac, infill drilling, and a greenfield development plan with variations on well spacing and completion design. The observation shows tighter well spacing or cluster spacing accelerates the oil production in early time, while yielding similar long term oil recovery and shows a combination of refrac and infill drilling yields a 21% incremental oil production beyond the base case.
This study provides valuable information about the workflow to develop tight oil plays by describing a detailed case study. The result also sheds light on the optimized field development strategy for analogous fields.
This study presents a novel, integrated workflow to maximize recovery using PVT compositional modeling, history matching, and numerical reservoir simulation in a tight oil sand formation, the Second Bone Spring. Advancements in unconventional resource development have enabled the Delaware Basin to become highly significant. However, optimizing the development of each formation is still lacking in understanding. This study is one of multiple future studies over tight reservoirs in the Delaware Basin and exhibits a comprehensive approach. Properties that will be optimized are well spacing, reservoir parameters, and EOR feasibility.
To determine the behavior and optimize the development of each of these reservoirs, data from multiple sources was necessary. The data compiled consisted of reports from PVT analysis, completions design, petrophysical analysis, daily production and pressure, deviation surveys, structure and isopach maps, and well design. This data was then implemented into a 3D numerical reservoir simulator (CMG-GEM), first to confirm PVT output in a compositional simulation (CMG-WINPROP), then to simulate up to 20 years of production, and finally to use uncertainty analysis (CMG-CMOST) to optimize reservoir input parameters. Once a base case scenario was established, we then furthered our investigation of well spacing and EOR feasibility by setting up multiple different scenarios for each and running them for 20 years. EOR scenarios included 1-3 month huff-and-puff CO2, as well as low salinity water injection. Results are normalized per foot of completely lateral length and lab data is implemented in EOR simulations.
Our results confirm that reservoir parameters, once established after uncertainty analysis, have a large impact on both optimizing well spacing and EOR feasibility in the Second Bone Spring formation. With each well having very similar cluster spacing, proppant amount and type, and fracturing fluid and type, up to 250 feet of inter-well spacing is unaccounted for. Optimized models show that closer spacing of at least 150 feet can increase EUR estimates an average of 11.25%. An increase of 5-17% recovery is observed once a smaller spacing is implemented. EOR models showed that CO2 and low salinity water injection are viable candidates for the formation (7.25-9% increase for CO2, 6.25% for LSWI).
This integrated study refines our reservoir parameter estimates and helps identify potential to maximize recovery. It suggests that a tighter spacing is necessary to cover a larger portion of the reservoir, as well as showing that EOR is feasible. An improved understanding of the entire reservoir leads to better production and economic estimates.
Burgan Marrat, a deep carbonate reservoir was transferred from exploration to development team for an accelerated production of the newly discovered oil. This multi-billion barrel reservoir is spread over 450 km2, has more than 40 faults, 8 compartments with large variation in oil-water contact and reservoir/fluid characteristics. The objective of this work is to understand the key uncertainties and quantify their impact on the reservoir offtake rate and oil recovery by conducting uncertainty assessment.
An interdisciplinary team identified the key uncertainty parameters expected to have significant impact on the reservoir development. The range and probability distribution law for each parameter was set considering the uncertainties due to limited measurements or variation in interpretations. A Response Surface Model (RSM) was created to evaluate the uncertainties by using a base dynamic model and applying an appropriate experimental design, which allowed to efficiently study the uncertainty space with a feasible number of simulations. Using the RSM, the primary effects and interaction between parameters were quantified to rank the uncertainties based on their impact on field production.
Key uncertainty parameters were identified including eight OWCs, six fault transmissibilities, horizontal and vertical permeability multipliers, and porosity multiplier. Latin Hypercube was found to be the appropriate Experimental Design for the study considering 17 parameters and the need of building a reliable RSM that includes interactions between them. The design recommended 155 simulation cases, which were prepared and submitted automatically by the software.
Multi-time Responses were analyzed qualitatively to identify the top 5 uncertainties having material impact on field production over 20 years considering 6 existing wells and 30 new well locations. The RSM quantitative evaluation showed three parameters (OWC2, OWC4 and OWC1) having a total effect on the response higher than 10%; followed by PERMX and OWC3 with less than 5%. The other 12 parameters have total effects less than 2%, and the interactions effect is less than 0.5% for any interaction between two parameters. Contrary to the intuition, none of the faults proved impact on the reservoir production.
The results prove very useful to make a right development and appraisal strategy in early life of the reservoir. The new well locations can be ranked and prioritized to optimize the development and effectively appraise the areas with high risks.
Uncertainty assessment has value throughout the life of the reservoir. However, this study indicates that its application in early life of the reservoir can bring immense value. An uncertainty analysis on the reservoir production helps in decision-making regarding the number of wells and their locations to reach a target production by managing the risks.
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.
Immiscible water-alternating-gas (iWAG) flooding is often considered as a tertiary recovery technique in waterflooded or about-to-be waterflooded reservoirs to increase oil recovery due to better mobility control and potentially favorable hysteretic changes to phase relative permeabilities. In such cases, typically, reservoir simulation models already exist and have been calibrated, often modifying saturation functions during the history matching stage. However, to utilize such models in forecasting iWAG performance, additional parameters may be required. These can be acquired by simulation of WAG coreflood experiments. While in many published cases, the parameter values obtained from matching experimental results are used without modification, this may not be advisable since the parameters are only valid at the core scale at which they were obtained. This paper discusses the challenge of systematically upscaling WAG parameters obtained at core scale to an existing full field model.
In this work, we use a multi-stage upscaling process from core scale to full field scale. The first stage uses a core scale model to match ‘representative’ core flood experiments and obtain WAG parameters. The second uses a well-to-well high-resolution 1D section of the full field model populated using gridblocks of core size to generate ‘reference’ WAG performance using the unaltered WAG parameters obtained from core. The third stage uses a similar 1D model but populated using gridblocks at full field model resolution to match the results from the reference model while adjusting the WAG parameters as little as possible. Finally, a model using the full field model resolution as well as the full field relative permeability functions which, it is assumed, have been tuned to match the history and account for dispersion is used to match the reference model results and obtain final upscaled WAG parameters.
The upscaled WAG parameters obtained at the end of this multi-stage process can be used at the field scale. This process allows clear quantification of the uncertainty associated with the upscaling process. Simulations at the third stage showed that once the full field to core scale grid size ratio exceeded a certain point (2500:1), there was a marked increase in the difference between upscaled and reference model results. It was found that if WAG parameters were changed in the full field model resolution model in order to match recovery results in the reference model, Land's parameter could change by up to 10% and relative permeability reduction factor could increase by up to 30% although it is expected that this will vary from case to case. It is therefore recommended to identify and use full field model resolutions to as close to the threshold as possible. The practice of using the core scale iWAG parameters in the full field model directly could under-estimate actual recovery, and overestimate injectivity. When considering the WAG mechanism alone, the value of the recovery underestimate increasing with pore volumes injected and, in our case, by up to 7% after injecting 1 pore volume of fluid.
This multi-stage simulation approach helps identify the adjustments required and uncertainties associated with simulating iWAG flooding in reservoir models. This approach utilizes options widely present in commercially available finite difference simulators, addresses the challenge of utilizing existing pseudo functions and provides a practical methodology through which iWAG performance forecasting can be improved.
Gao, Guohua (Shell Global Solutions (US)) | Vink, Jeroen C. (Shell Global Solutions International) | Chen, Chaohui (Shell International Exploration and Production) | Araujo, Mariela (Shell Global Solutions (US)) | Ramirez, Benjamin A. (Shell International Exploration and Production) | Jennings, James W. (Shell International Exploration and Production) | El Khamra, Yaakoub (Shell Global Solutions (US)) | Ita, Joel (Shell Global Solutions (US))
Uncertainty quantification of production forecasts is crucially important for business planning of hydrocarbon-field developments. This is still a very challenging task, especially when subsurface uncertainties must be conditioned to production data. Many different approaches have been proposed, each with their strengths and weaknesses. In this work, we develop a robust uncertainty-quantification work flow by seamless integration of a distributed-Gauss-Newton (GN) (DGN) optimization method with a Gaussian mixture model (GMM) and parallelized sampling algorithms. Results are compared with those obtained from other approaches.
Multiple local maximum-a-posteriori (MAP) estimates are determined with the local-search DGN optimization method. A GMM is constructed to approximate the posterior probability-density function (PDF) by reusing simulation results generated during the DGN minimization process. The traditional acceptance/rejection (AR) algorithm is parallelized and applied to improve the quality of GMM samples by rejecting unqualified samples. AR-GMM samples are independent, identically distributed samples that can be directly used for uncertainty quantification of model parameters and production forecasts.
The proposed method is first validated with 1D nonlinear synthetic problems with multiple MAP points. The AR-GMM samples are better than the original GMM samples. The method is then tested with a synthetic history-matching problem using the SPE01 reservoir model (Odeh 1981; Islam and Sepehrnoori 2013) with eight uncertain parameters. The proposed method generates conditional samples that are better than or equivalent to those generated by other methods, such as Markov-chain Monte Carlo (MCMC) and global-search DGN combined with the randomized-maximum-likelihood (RML) approach, but have a much lower computational cost (by a factor of five to 100). Finally, it is applied to a real-field reservoir model with synthetic data, with 235 uncertain parameters. AGMM with 27 Gaussian components is constructed to approximate the actual posterior PDF. There are 105 AR-GMM samples accepted from the 1,000 original GMM samples, and they are used to quantify the uncertainty of production forecasts. The proposed method is further validated by the fact that production forecasts for all AR-GMM samples are quite consistent with the production data observed after the history-matching period.
The newly proposed approach for history matching and uncertainty quantification is quite efficient and robust. The DGN optimization method can efficiently identify multiple local MAP points in parallel. The GMM yields proposal candidates with sufficiently high acceptance ratios for the AR algorithm. Parallelization makes the AR algorithm much more efficient, which further enhances the efficiency of the integrated work flow.