EVALUATION AND MANAGEMENT OF THIN OIL COLUMN RESERVOIRS IN AUSTRALIA

Thin oil columns represent a common and important class of hydrocarbon reserve which are notoriously difficult to evaluate and produce. This paper provides case studies of examples of these reservoirs in Australia and summarises the production methods, well performance and recovery efficiencies.Thin oil column reservoirs are defined here as reservoirs which will cone both water and gas when produced at commercial rates. The oil zone can have a pancake or rim geometry. Examples within Australia include Bream and Snapper (Gippsland Basin), South Pepper and Chervil (Carnarvon Basin), Chookoo (Eromanga Basin) and Taylor (Surat Basin).Parameters which are particularly important in defining the performance of these reservoirs are: horizontal and vertical permeability, column height, stratigraphie dip, well spacing, and oil viscosity. High horizontal permeability is more critical than in other reservoir types as it controls the effectiveness of gravitational forces in opposing coning and other unwanted flows by reducing pressure gradients. Low vertical permeability mitigates coning but can limit across strike drainage in dipping strata. Oil viscosity is also particularly important, even when the mobility ratio is favourable, as it controls the gas/oil ratio and water cut during coning.As coning (by definition) is inevitable the key production issue is gas cap management. The main options are:Limit gas coning by controlling completion depth and production rates.Allow gas cap shrinkage and 'chase' the oil column upwards via recompletions.Reinject gas to control gas-oil contact position.For the latter two options in particular, ultimate reserves are a strong function of the capacity of the installed production facilities, especially offshore, where fixed operating costs are high. When gas cap management is not compromised, reserves increase with higher total fluid withdrawal rates. Examples of the various gas cap management and production strategies are included.Both horizontal (South Pepper, Bream) and conventional (Chookoo, Taylor) completion techniques have been applied to thin oil column reservoirs in Australia. Horizontal completions can increase productivity, mitigate coning and increase the well drainage areas (particularly if drilled across dip in heterogeneous reservoirs). However, horizontal completions are particularly vulnerable to poor cement jobs, natural fractures and undesirable fluid contact movements.A variety of other completion techniques have been tried worldwide in thin oil columns with mixed success. These include multiple completions in the water, oil and/or gas to allow separate production, and injection of fluids to make permeability barriers or alter relative permeability.A number of scaling rules are included to assist in using offset field data for evaluation of thin oil column reservoirs. Improved understanding of these complex reservoirs will maximise their economic potential.

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Investigation and Optimization of the Effects of Geologic Parameters on the Performance of Gravity-Stable Surfactant Floods

SPE Journal ◽

10.2118/178915-pa ◽

2016 ◽

Vol 21 (03) ◽

pp. 761-775 ◽

Cited By ~ 2

Author(s):

Shayan Tavassoli ◽

Gary A. Pope ◽

Kamy Sepehrnoori

Keyword(s):

Salinity Gradient ◽

Horizontal Wells ◽

Sensitivity Study ◽

Third Order ◽

Fine Grid ◽

Heterogeneous Reservoirs ◽

Vertical Permeability ◽

Well Spacing ◽

Optimization Schemes ◽

Very High

Summary A systematic simulation study of gravity-stable surfactant flooding was performed to understand the conditions under which it is practical and to optimize its performance. Different optimization schemes were introduced to minimize the effects of geologic parameters and to improve the performance and the economics of surfactant floods. The simulations were carried out by use of horizontal wells in heterogeneous reservoirs. The results show that one can perform gravity-stable surfactant floods at a reasonable velocity and with very-high sweep efficiencies for reservoirs with high vertical permeability. These simulations were carried out with a 3D fine grid and a third-order finite-difference method to accurately model fingering. A sensitivity study was conducted to investigate the effects of heterogeneity and well spacing. The simulations were performed with realistic surfactant properties on the basis of laboratory experiments. The critical velocity for a stable surfactant flood is a function of the microemulsion (ME) viscosity, and it turns out there is an optimum value that one can use to significantly increase the velocity and still be stable. One can optimize the salinity gradient to gradually change the ME viscosity. Another alternative is to inject a low-concentration polymer drive following the surfactant slug (without polymer). Polymer complicates the process and adds to its cost without a significant benefit in most gravity-stable surfactant floods, but an exception is when the reservoir is highly layered. The effect of an aquifer on gravity-stable surfactant floods was also investigated, and strategies were developed for minimizing its effect on the process.

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History to Prediction Transition Calibration of Reservoir Simulation Model Ensemble

10.2118/205212-ms ◽

2021 ◽

Author(s):

Bjørn Egil Ludvigsen ◽

Mohan Sharma

Keyword(s):

Simulation Model ◽

Reservoir Simulation ◽

History Matching ◽

Simulation Models ◽

Productivity Index ◽

Model Parameters ◽

Gas Oil ◽

Well Performance ◽

Reservoir Simulation Model ◽

Water Cut

Abstract Well performance calibration after history matching a reservoir simulation model ensures that the wells give realistic rates during the prediction phase. The calibration involves adjusting well model parameters to match observed production rates at specified backpressure(s). This process is usually very time consuming such that the traditional approaches using one reservoir model with hundreds of high productivity wells would take months to calibrate. The application of uncertainty-centric workflows for reservoir modeling and history matching results in many acceptable matches for phase rates and flowing bottom-hole pressure (BHP). This makes well calibration even more challenging for an ensemble of large number of simulation models, as the existing approaches are not scalable. It is known that Productivity Index (PI) integrates reservoir and well performance where most of the pressure drop happens in one to two grid blocks around well depending upon the model resolution. A workflow has been setup to fix transition by calibrating PI for each well in a history matched simulation model. Simulation PI can be modified by changing permeability-thickness (Kh), skin, or by applying PI multiplier as a correction. For a history matched ensemble with a range in water-cut and gas-oil ratio, the proposed workflow involves running flowing gradient calculations for a well corresponding to observed THP and simulated rates for different phases to calculate target BHP. A PI Multiplier is then calculated for that well and model that would shift simulation BHP to target BHP as local update to reduce the extent of jump. An ensemble of history matched models with a range in water-cut and gas-oil ratio have a variation in required BHPs unique to each case. With the well calibration performed correctly, the jump observed in rates while switching from history to prediction can be eliminated or significantly reduced. The prediction thus results in reliable rates if wells are run on pressure control and reliable plateau if the wells are run on group control. This reduces the risk of under/over-predicting ultimate hydrocarbon recovery from field and the project's cashflow. Also, this allows running sensitivities to backpressure, tubing design, and other equipment constraints to optimize reservoir performance and facilities design. The proposed workflow, which dynamically couple reservoir simulation and well performance modeling, takes a few seconds to run for a well, making it fit-for-purpose for a large ensemble of simulation models with a large number of wells.

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Data-Driven Causal Analyses of Parent-Child Well Interactions for Well Spacing Decisions

10.2118/204165-ms ◽

2021 ◽

Author(s):

Rohan Sakhardande ◽

Deepak Devegowda

Keyword(s):

Causal Inference ◽

Randomized Clinical Trials ◽

Drug Efficacy ◽

Complex Problem ◽

Petroleum Engineering ◽

Well Performance ◽

Well Spacing ◽

The Impact ◽

Naive Approach ◽

Parent Child

Abstract The analyses of parent-child well performance is a complex problem depending on the interplay between timing, completion design, formation properties, direct frac-hits and well spacing. Assessing the impact of well spacing on parent or child well performance is therefore challenging. A naïve approach that is purely observational does not control for completion design or formation properties and can compromise well spacing decisions and economics and perhaps, lead to non-intuitive results. By using concepts from causal inference in randomized clinical trials, we quantify the impact of well spacing decisions on parent and child well performance. The fundamental concept behind causal inference is that causality facilitates prediction; but being able to predict does not imply causality because of association between the variables. In this study, we work with a large dataset of over 3000 wells in a large oil-bearing province in Texas. The dataset includes several covariates such as completion design (proppant/fluid volumes, frac-stages, lateral length, cluster spacing, clusters/stage and others) and formation properties (mechanical and petrophysical properties) as well as downhole location. We evaluate the impact of well spacing on 6-month and 1-year cumulative oil in four groups associated with different ranges of parent-child spacing. By assessing the statistical balance between the covariates for both parent and child well groups (controlling for completion and formation properties), we estimate the causal impact of well spacing on parent and child well performance. We compare our analyses with the routine naïve approach that gives non-intuitive results. In each of the four groups associated with different ranges of parent-child well spacing, the causal workflow quantifies the production loss associated with the parent and child well. This degradation in performance is seen to decrease with increasing well spacing and we provide an optimal well spacing value for this specific multi-bench unconventional play that has been validated in the field. The naïve analyses based on simply assessing association or correlation, on the contrary, shows increasing child well degradation for increasing well spacing, which is simply not supported by the data. The routinely applied correlative analyses between the outcome (cumulative oil) and predictors (well spacing) fails simply because it does not control for variations in completion design over the years, nor does it account for variations in the formation properties. To our knowledge, there is no other paper in petroleum engineering literature that speaks of causal inference. This is a fundamental precept in medicine to assess drug efficacy by controlling for age, sex, habits and other covariates. The same workflow can easily be generalized to assess well spacing decisions and parent-child well performance across multi-generational completion designs and spatially variant formation properties.

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A Unified Parameter to Represent Reservoir Heterogeneity

10.2118/208630-stu ◽

2021 ◽

Author(s):

Nandana Ramabhadra Agastya

Keyword(s):

Water Saturation ◽

Pressure Ratio ◽

Universal Method ◽

Permeability Ratio ◽

Initial Water ◽

Vertical Permeability ◽

Radar Chart ◽

Horizontal Permeability ◽

Water Cut ◽

Thief Zone

Abstract We aim to find a universal method and/or parameter to quantify impact of overall heterogeneity on waterflood performance. For this purpose, we combined the Lorenz coefficient, horizontal permeability to vertical permeability ratio, and thief zone permeability to average permeability ratio, with a radar chart. The area of the radar chart serves as a single parameter to rank reservoirs according to heterogeneity, and correlates to waterflood performance. The parameters investigated are vertical and horizontal permeability. Average porosity, initial water saturation, and initial diagonal pressure ratio are kept constant. Computer based experiments are used over the course of this entire research. We conducted permeability studies that demonstrate the effects of thief zones and crossflow. After normalizing these parameters into a number between 0 and 1, we then plot them on a radar chart. A reservoir's overall degree of heterogeneity can be inferred using the radar chart area procedure discussed in this study. In general, our simulations illustrate that the larger the radar chart area, the more heterogenous the reservoir is, which in turn yields higher water cut trends and lower recovery factors. Computer simulations done during this study also show that the higher the Lorenz coefficient, the higher the probability of a thief zone to exist. Simulations done to study crossflow also show certain trends with respect to under tonguing and radar chart area.

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Comparison Between Homogenous and Heterogeneous Reservoirs: A Parametric Study of Water Coning Phenomena

UKH Journal of Science and Engineering ◽

10.25079/ukhjse.v5n1y2021.pp119-131 ◽

2021 ◽

Vol 5 (1) ◽

pp. 119-131

Author(s):

Frzan F. Ali ◽

Maha R. Hamoudi ◽

Akram H. Abdul Wahab

Keyword(s):

Production Rate ◽

Oil Production ◽

Water Production ◽

Effective Parameters ◽

Radial Cross Section ◽

Heterogeneous Reservoirs ◽

Water Breakthrough ◽

Water Coning ◽

Water Cut ◽

Reservoir Simulator

Water coning is the biggest production problem mechanism in Middle East oil fields, especially in the Kurdistan Region of Iraq. When water production starts to increase, the costs of operations increase. Water production from the coning phenomena results in a reduction in recovery factor from the reservoir. Understanding the key factors impacting this problem can lead to the implementation of efficient methods to prevent and mitigate water coning. The rate of success of any method relies mainly on the ability to identify the mechanism causing the water coning. This is because several reservoir parameters can affect water coning in both homogenous and heterogeneous reservoirs. The objective of this research is to identify the parameters contributing to water coning in both homogenous and heterogeneous reservoirs. A simulation model was created to demonstrate water coning in a single- vertical well in a radial cross-section model in a commercial reservoir simulator. The sensitivity analysis was conducted on a variety of properties separately for both homogenous and heterogeneous reservoirs. The results were categorized by time to water breakthrough, oil production rate and water oil ratio. The results of the simulation work led to a number of conclusions. Firstly, production rate, perforation interval thickness and perforation depth are the most effective parameters on water coning. Secondly, time of water breakthrough is not an adequate indicator on the economic performance of the well, as the water cut is also important. Thirdly, natural fractures have significant contribution on water coning, which leads to less oil production at the end of production time when compared to a conventional reservoir with similar properties.

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Different Nonlinear Regression Techniques and Sensitivity Analysis as Tools to Optimize Oil Viscosity Modeling

Resources ◽

10.3390/resources10100099 ◽

2021 ◽

Vol 10 (10) ◽

pp. 99

Author(s):

Dicho Stratiev ◽

Svetoslav Nenov ◽

Dimitar Nedanovski ◽

Ivelina Shishkova ◽

Rosen Dinkov ◽

...

Keyword(s):

Sensitivity Analysis ◽

Nonlinear Regression ◽

Information Criterion ◽

Least Square ◽

Gas Oil ◽

Oil Viscosity ◽

Prediction Ability ◽

Regression Techniques ◽

Comparable Accuracy ◽

Relative Errors

Four nonlinear regression techniques were explored to model gas oil viscosity on the base of Walther’s empirical equation. With the initial database of 41 primary and secondary vacuum gas oils, four models were developed with a comparable accuracy of viscosity calculation. The Akaike information criterion and Bayesian information criterion selected the least square relative errors (LSRE) model as the best one. The sensitivity analysis with respect to the given data also revealed that the LSRE model is the most stable one with the lowest values of standard deviations of derivatives. Verification of the gas oil viscosity prediction ability was carried out with another set of 43 gas oils showing remarkably better accuracy with the LSRE model. The LSRE was also found to predict better viscosity for the 43 test gas oils relative to the Aboul Seoud and Moharam model and the Kotzakoulakis and George.

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Review of Practical Experience by Polymer Flooding at Daqing

SPE Reservoir Evaluation & Engineering ◽

10.2118/114342-pa ◽

2009 ◽

Vol 12 (03) ◽

pp. 470-476 ◽

Cited By ~ 73

Author(s):

Dongmei Wang ◽

Huanzhong Dong ◽

Changsen Lv ◽

Xiaofei Fu ◽

Jun Nie

Keyword(s):

Oil Production ◽

Polymer Flooding ◽

Oil Field ◽

High Permeability ◽

Sweep Efficiency ◽

Molecular Weights ◽

Well Spacing ◽

Polymer Flood ◽

Daqing Oil Field ◽

Water Cut

Summary This paper describes successful practices applied during polymer flooding at Daqing that will be of considerable value to future chemical floods, both in China and elsewhere. On the basis of laboratory findings, new concepts have been developed that expand conventional ideas concerning favorable conditions for mobility improvement by polymer flooding. Particular advances integrate reservoir-engineering approaches and technology that is basic for successful application of polymer flooding. These include the following:Proper consideration must be given to the permeability contrast among the oil zones and to interwell continuity, involving the optimum combination of oil strata during flooding and well-pattern design, respectively;Higher polymer molecular weights, a broader range of polymer molecular weights, and higher polymer concentrations are desirable in the injected slugs;The entire polymer-flooding process should be characterized in five stages--with its dynamic behavior distinguished by water-cut changes; -Additional techniques should be considered, such as dynamic monitoring using well logging, well testing, and tracers; effective techniques are also needed for surface mixing, injection facilities, oil production, and produced-water treatment; andContinuous innovation must be a priority during polymer flooding. Introduction China's Daqing oil field entered its ultrahigh-water-cut period after 30 years of exploitation. Just before large-scale polymer-flooding application, the average water-cut was more than 90%. The Daqing oil-field is a large river-delta/lacustrine facies, multilayered with complex geologic conditions and heterogeneous sandstone in an inland basin. After 30 years of waterflooding, many channels and high-permeability streaks were identified in this oil field (Wang and Qian 2002). Laboratory research began in the 1960s, investigating the potential of enhanced-oil-recovery (EOR) processes in the Daqing oil field. After a single-injector polymer flood with a small well spacing of 75 m in 1972, polymer flooding was set on pilot test. During the late 1980s, a pilot project in central Daqing was expanded to a multiwell pattern with larger well spacing. Favorable results from these tests--along with extensive research and engineering from the mid-1980s through the 1990s--confirmed that polymer flooding was the preferred method to improve areal- and vertical-sweep efficiency at Daqing and to provide mobility control (Wang et al. 2002, Wang and Liu 2004). Consequently, the world's largest polymer flood was implemented at Daqing, beginning in 1996. By 2007, 22.3% of total production from the Daqing oil field was attributed to polymer flooding. Polymer flooding boosted the ultimate recovery for the field to more than 50% of original oil in place (OOIP)--10 to 12% OOIP more than from waterflooding. At the end of 2007, oil production from polymer flooding at the Daqing oil field was more than 10 million tons (73 million bbl) per year (sustained for 6 years). The focus of this paper is on polymer flooding, in which sweep efficiency is improved by reducing the water/oil mobility ratio in the reservoir. This paper is not concerned with the use of chemical gel treatments, which attempt to block water flow through fractures and high-permeability strata. Applications of chemical gel treatments in China have been covered elsewhere (Liu et al. 2006).

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A Comprehensive Machine Learning Approach for Quantitatively Analyzing Development Performance and Optimization for a Heterogeneous Carbonate Reservoir in Middle East

10.2118/208529-ms ◽

2021 ◽

Author(s):

Ruijie Huang ◽

Chenji Wei ◽

Baohua Wang ◽

Baozhu Li ◽

Jian Yang ◽

...

Keyword(s):

Machine Learning ◽

Principal Component ◽

Optimal Number ◽

Gas Production ◽

Carbonate Reservoir ◽

Multidimensional Data ◽

Well Performance ◽

Reference Case ◽

Evaluation Approach ◽

Water Cut

Abstract Compared with conventional reservoir, the development efficiency of the carbonate reservoir is lower, because of the strong heterogeneity and complicated reservoir structure. How to accurately and quantitatively analyze development performance is critical to understand challenges faced, and to propose optimization plans to improve recovery. In the study, we develop a workflow to evaluate similarities and difference of well performance based on Machine Learning methods. A comprehensive Machine Learning evaluation approach for well performance is established by utilizing Principal Component Analysis (PCA) in combination with K-Means clustering. The multidimensional dataset used for analysis consists of over 15 years dynamic surveillance data of producers and static geology parameters of formation, such as oil/water/gas production, GOR, water cut (WC), porosity, permeability, thickness, and depth. This approach divides multidimensional data into several clusters by PCA and K-Means, and quantitatively evaluate the well performance based on clustering results. The approach is successfully developed to visualize (dis)similarities among dynamic and static data of heterogeneous carbonate reservoir, the optimal number of clusters of 27-dimension data is 4. This method provides a systematic framework for visually and quantitatively analyzing and evaluating the development performance of production wells. Reservoir engineers can efficiently propose targeted optimization measures based on the analysis results. This paper offers a reference case for well performance clustering and quantitative analysis and proposing optimization plans that will help engineers make better decision in similar situation.

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