Poor conformance & injectivity sustenance is a major concern of Mangala, Bhagyam & Aishwarya (MBA) fields. The presence of high permeability streaks or thief layers between injection and production wells typically results in pre-mature water breakthrough, high water cut and deficient volumetric sweep. As a result, significant oil volumes in the reservoir may not be contacted by the injection fluid. Another concern is of low VRR (Voidage Replacement Ratio) in some of the layers due to reduced injectivity in those sands. Consequently, it has led to poor recovery from those sands. It is also a growing problem with the polymer deposition taking place in the wellbore particularly Mangala (undergoing full-field polymer flooding), leading to challenging wellbore cleanup operations. Several methods have been used in the past, both mechanical and chemical to improve the treatment fluids during stimulation. In this paper, we introduce a novel technique; Radial Jet Drilling (RJD) for Injection & Conformance Improvement which is practical & effective and has resulted in injection improvement of greater than 100% and at-least 3x sustained treatment result over conventional techniques performed earlier. The technique involves running in hole the RJD deflector shoe on jointed pipe (typically 2-3/8") to the target depth & correlated. This is followed up with high pressure coiled tubing to rig up over the jointed pipe string and perform operations in two steps: Milling to establish Casing exit Jetting to create lateral of desired length (30 to 100m) Both operations require separate runs with different downhole assemblies on coiled tubing. The setup allows the operator to jet with fluid of choice depending upon the geological & well fluid understanding. The selection of depths to create desired laterals depends upon sand thickness, formation dip, sand continuity, well deviation, cement quality & few other factors which shall be discussed in this paper. As a result of the created laterals, the injection fluid experiences low pressure drops and bypasses damaged zones/permeability barriers; which ultimately results in improved injection & conformance leading to better sweep efficiency. The other advantage of the technique is the relatively easy integration of tool with existing infrastructure i.e. small horsepower rigs. This paper will document the physics behind the technique, treatment design considerations and implementation for stimulations performed across the fields using Radial jet drilling. Particular attention is paid to multiple injector well stimulation case studies from these fields, the challenges faced, the solutions implemented, results & inferences drawn using post job surveillance. The results observed across the field w.r.t injection & its sustenance performance is consistently greater over conventional methods used earlier.

CO 2 injection has proven to be a promising technology in enhancing oil recovery in low-permeablility carbonate reservoirs, especially in miscible flooding. How to demonstrate near-miscible/miscible mechanisms and their influence on production performance is a difficult issue to deal with in a compositional simulation. In this paper, considerable research has been conducted on compositional models, especially miscible gas flooding models, by identifying the near-miscible/miscible state and its influence on relative permeability. This paper describes a multi-component, three-phase, compositional model for simulating miscible CO 2 flooding problems. An EOS equation is constructed for phase equilibrium and property calculations. Gibbs stability model is developed to determine whether a hydrocarbon mixture at a particular temperature and pressure is more stable in a single-phase state or in a two-phase state. An interfacial tension (IFT) weighted relatively permeability model is developed to calculate the relative permeability in a near-miscible state. With the help of the Gibbs and Kr models, miscible CO 2 flooding in a carbonate reservoir is simulated to evaluate the miscible and immiscible state and demonstrate the production performance. The Gibbs stability model determines the tangent plane of the Gibbs energy surface at the mixture composition and parallel tangent planes at possible incipient phase compositions. If any of the parallel tangent planes lie below the tangent plane of the mixture composition, a two-phase state will exist. The simulation shows that a Gibbs stability model in CO 2 injection can identify whether the hydrocarbon mixture (oil) and CO 2 at reservoir pressure and temperature is in one phase state (miscible) or in a two-phase state (immiscible). The IFT weighted Kr model determines an interpolation between immiscible and miscible states using a weighting function, which is a function of IFT with a specified critical gas/oil IFT to control the contribution of the near-miscible effect on kr. The compositional simulation shows that CO 2 becomes miscible with a hydrocarbon mixture under the condition of CO 2 multi-contact with the hydrocarbon mixture in the reservoir pressure and temperature. Three areas exist between the injector and the producer which are the immiscible area (IFT: about initial value), near-miscible area (IFT: 0.5~2) and miscible area (IFT: lower than 0.5). The residual oil saturation of the miscible CO 2 flooding area is around 5%. Oil recovery is enhanced compared with the previous hydrocarbon gas injection. CO 2 injection represents a promising technology to improve production performance and to enhance oil recovery for the carbonate reservoir mentioned in the paper.

One of the keys to successful and environmentally responsible well stimulation programs in coal seam gas development is to establish consistent procedures for the safeguarding, planning and executing activities across multiple wells. The aim of this paper is to show how a novel application of petrophysical program scripting can be used to make the stimulation process more efficient, consistent and compliant across assets with varying requirements. A macro embedded in petrophysical evaluation software applies a series of rules to rank coals by thickness, allocate a series of perforations and stimulation type based upon coal rank and spacing and then produces actionable treatment schedules which are seamlessly implemented in well stimulation operations at well sites. To do this, the macro grades all coals within the well by thickness based upon a cut-off on the density log, with the thickest coal being graded highest. The macro then identifies the top ranked coal and places perforations based on user defined logic, geological information from offset wells, permeability attributes of the target coal layer(s), depth and vertical separation between adjoining coal targets. Based on the stimulation type assigned, a stimulation schedule is generated that includes estimates of fluid volumes, proppant volumes, injection rates, proppant ramp type and stipulates flush conditions (over-flush or under-flush). Coals thicker than a maximum perforation size are perforated in an upper, middle and lower configuration. Most coals are thinner than the maximum allowable perforation interval and so the macro looks up and down the borehole to include thinner coals within a potential perforation window. The system then generates the stimulation schedule as described above. The macro continues to allocate perforations and stimulation schedules for each validated coal interval and sequentially tries to maximise the total target coal interval along the wellbore. Certain environmental constraints are included in the macro logic to maintain local and regional commitments. For example, coal zones in proximity of permeable non-coal layers i.e. interburden are automatically excluded from stimulation. Multiple advantages of this system have been realised including, a) effective QA/QC as outputs can be directly plotted against the well logs giving the user a quick and easy visual check b) actionable instructions that site based teams can execute including exact perforation depths and stimulation schedules c) provide realistic materials and costs estimates that ensure efficient planning and logistics, d) monitor and document any variations between allocated schedule versus actual execution, e) provide estimate of expected net coal connectivity at a well, development package and asset level which feeds into production and recovery forecasts, f) plan future optimisation studies or pilots and g) most importantly offers a consistent, efficient and compliant framework that can be applied across multiple assets, engineering teams and service providers. This paper focuses on capabilities and advantages of using a macro to automate stimulation design allocation for CSG multi-well (>100 wells) assets. Details of individual stimulation designs for Walloons Coal measures are mentioned in other publications ( Kirk-Burnnand et al., 2015 and Flottmann et al. 2018 ) and hence not covered here.

Bulk permeability of coal is a critical parameter in coalbed methane (CBM) or coal seam gas (CSG) well completion designs and field development planning. The estimation of permeability can be made by well testing either during drilling or production; however, well tests are costly, time sensitive and resource-intensive. Therefore, field-wide estimates are often dependent on production data history-matching, which has a high degree of uncertainty. In this paper, we present a new attempt to apply machine learning approach to estimate coal permeability using drilling data. We first extract important parameters from well test analyses, which are obtained using a packer element testing (PET) tool from four wells in the Surat Basin, Australia. Then drilling data from the wells are processed and fed into different artificial neural networks (ANNs), which include multi-layer perceptrons (MLP), convolutional neural network (CNN), and recurrent neural network (RNN). Two types of models are constructed: regression model with permeability values, and classification model with permeability class intervals (i.e., low, medium, and high permeability values). The evaluation metrics include R 2 for regression models and confusion matrix for classification models. Results show that the drilling mud losses are generally higher in coal layers and lower in interburden formations. The predicted medium and high coal permeabilities from MLP and CNN are in good agreement with measured permeability values from PET data. For the classification task, the CNN achieved an overall accuracy of 99%. Thus, an improved coal permeability map with a higher resolution and less calibration against PET data can be developed quickly to aid production data history matching. The developed machine learning model demonstrated a potential to be applied to new wells to predict the coal permeability for the Surat Basin as well as other CSG appraisal projects. This model can allow more rapid optimisation of well spacing, improved downhole pump design, more targeted well stimulation, and overall project economics.

A multi-chamber, finite-difference dynamic model assumed pore-scale reservoir properties and replicated laboratory processes to determine oil-water transition zone characteristics: (1) imbibition residual oil saturation to water flood (distributed as a function of rock properties); (2) imbibition oil-water relative permeability (according to both steady-state and unsteady-state calculations); and, (3) microscopic oil recovery factor as a function of height above free water level. A priori, scaled capillary pressure hysteresis and segregated flow conditions governed pore-scale flow behaviour (initial mobile saturation scaled 0-1) assuming a pipeline network analogue. Although an oil-water system was modelled the process could be extended to other fluid phases. The dynamic model was divided into five chambers replicating two experiments. Flow between chambers was regulated by transmissibility multipliers (open/shut). Replicating a core displacement experiment, an oil source rock "drained" (flooded) the central core chamber to form the oil-water transition zone; water then imbibed into the core chamber displacing mobile oil into a shallower effluent chamber; capillary pressure hysteresis defined the imbibition residual oil saturation to water flood. In a second experiment, the central core chamber was subdivided into zones, each comprising five model layers, and the imbibition oil-water relative permeability was determined by flooding the quiesced core chamber situated between high/low-pressure chambers (source/sink). Both steady-state and unsteady-state relative permeability were determined across the oil-water transition zone. The Digital Core Laboratory (DCL) determined the distribution of imbibition residual oil saturation as a function of: wettability assumption; rock properties; and, proximity to the free water level. For a range of wettability assumptions, imbibition oil-water relative permeability was determined across the transition zone, on a zonal basis, from top core chamber (top reservoir) down to the free water level. Summary of Results: (1) The imbibition residual oil saturation (ISorw) decreased with depth toward the free water level, while the drainage irreducible water saturation (Swir) increased with depth; (2) ISorw was found to be strongly influenced by the wettability assumption, contrasting with the uniformity of Swir; (3) Microscopic oil recovery factor was determined across the transition zone as a function of both proximity to the free water level and the wettability assumption; except for the oil-wet case, microscopic oil recovery factor increased with depth toward the free water level. The novel approach was the multi-chambered design of the finite difference model forming a Digital Core Laboratory (DCL). Tuning the model to a Special Core Analysis (SCAL) data set, with the objective of filling SCAL data gaps, is expected to be one application.

Based on three established commercial test cases, this paper documents their polymer flood process performance, revealing the impact of reservoir properties and heterogeneity on incremental recovery and dynamic performance. The key reservoir management measures for improving polymer flood sweeping efficiency are illustrated, which are particularly important for the successful application of chemical EOR techniques in heterogeneous sandstone reservoirs. The three cases produced oil with similar fluid properties from sandstone reservoirs. They all adopted 5-spot well patterns, which provide excellent examples for studying the impact of reservoir property and heterogeneity on incremental recovery. The producing reservoir sands in the three pilot represent two types. The former is of high permeability and low heterogenity. The latter is characterized by low permeability, multiple flow units, thin individual sand layers and high inter-layer permeability contrast. The authors reviewed and compared changes in water-cut and production rate performance versus pore volume of polymer solution injected during the process, as well as the applied well spacing, polymer utilization, and ultimate incremental recoveries. The two high quality reservoir cases adopted 22 ac well spacing, achieved an incremental recovery of 12.5% and 13.7%, a low polymer utilization of 1.08 kg/BO and 1.79 kg/BO, respectively, and a maximum water-cut reduction of 24.4%. In the low permeability case, although a much smaller well spacing of 11 ac was adopted, it achieved a relatively low incremental recovery of 8.2% and a maximum water-cut drop of 5.2%, with a polymer utilization of 2.51 kg/BO. Incremental EUR of individual producer through polymer flood is 49 MBO in the low permeability case, compared with 130 MBO in the high permeability reservoir sands. The three cases proved the effectiveness of fit-for-purpose reservoir management and conformance improvement techniques in improving polymer flood sweep efficiency to achieve high incremental recovery. Such techniques include hydraulic fracturing of low permeability sands, optimization of polymer solution concentration and molecular weight to adapt to different sand units with varied permeability, optimization of injection process to maintain a uniform pressure system, adjustment of injection direction to sweep by-passed remaining oil, and, zonal injection to improve polymer solution intake in low permeability sand units. The discussed cases proved the technical feasibility and economic viability of polymer flood in sandstone reservoirs with varied heterogeneities and revealed the impact of heterogeneity on performance and incremental recovery. The reservoir management and conformance improvement efforts during polymer flood process provided valuable lessons and best practices for similar applications.

The Jurassic Plover Formation is one of two reservoirs in the Ichthys Field, North West Shelf of Australia. It consists of fluvial to shallow-marine sandstones, shales and igneous rocks. The objective of this study is to build multiple scenario-based models to optimise development planning in preparation for the upcoming production phase. We have integrated data and interpretations of thin sections, cores, well logs and seismic data to create multiple geological concepts for the field and to identify key geological uncertainties. As the reservoir is geologically complex and many uncertainties were initially identified, it is essential to single out those uncertainties which have a significant impact on the development planning. We have established the key uncertainties and optimal model design for practical use through multi-disciplinary discussions and by running sensitivity models to check the production performance. A rock type (RT) scheme has been devised based on detailed petrographic observations and justified in terms of sedimentology and diagenesis. Using the scheme, a wide range of permeability variations in the sandstones has been captured and modelled. Environments of deposition (EOD) are firstly interpreted at core and well-log scales, then upscaled to the model zone scale. The EOD interpretations are laterally extended using lithology (sandstone, shale and igneous rock) probability maps derived from quantitative seismic interpretation (QSI). Multiple EOD scenarios are generated to capture the possible range of reservoir distributions. Each EOD is characterised by a unique net-sand porosity and RT proportion based on the well data. These values are used to define multiple possible porosity trends and RT proportions, guided by the EOD maps. The distribution and quality of the reservoir sandstones have been identified as key uncertainties. Another key uncertainty is reservoir compartmentalisation, thought to be mainly caused by sheet-like igneous intrusions. Subtle seismic lineaments are regarded as possible indications of such igneous intrusions, and multiple compartmentalisation scenarios have been prepared based on our understanding of igneous activity across the field. Reservoir structure and water saturation are also recognised as key uncertainties. Integrating the key uncertainties, we have established a practical modelling workflow and built multiple scenario-based models to cover a sufficient range of geological uncertainty. The workflow is also adaptive for future history matching, enabling us to flexibly edit the model properties under geological constraints. A decision tree for development planning, which defines a series of decisions for the well sequence depending on the well results, will be prepared based on the multiple scenario-based models delivered in this study. This will enable us to prepare for any potential decision-making in advance. The development planning will be continuously optimised throughout the production phase by simply selecting the scenario-based models most in line with the well results.

Formation evaluation including porosity and directional permeability assessment in complex carbonate formations is challenging due to the anisotropic and heterogeneous nature of the formation. The pore structure can be captured by digital rock technology, which makes the constriction of numerical whole core, and the numerical simulations of chemical enhanced oil recovery on digital rock possible. We introduce a new workflow to construct the digital rock based on micron-CT scanned sequential images of the carbonate sample. Firstly, we apply the modified Otsu's global thresholding method to convert the grey-scale micron-CT scanned images to binary images, and we systematically divide the whole core 3D segmented image into small blocks or representative elementary volume (totally 10143 blocks including inactive blocks). Then we estimate the porosity of each block, and use Lattice Boltzmann Method (LBM) to simulate fluid flow in randomly selected blocks and accurately compute the directional permeability of these blocks by simulating Darcy flow through them. The permeability tensor of other blocks is estimated by applying Kriging method based on the estimated porosity of all the blocks and the accurately calculated permeability tensor of the selected blocks. Then we simulate the polymer flooding on the constructed digital rock and show that the simulation process is computational efficient. We calculate the porosity and permeability of the constructed digital whole core and compare them with laboratory measurements. The image analyzed porosity of whole digital rock is comparable with laboratory measurements by only 1% difference. The simulated permeability value with calibration of the whole digital rock is also in good agreement with experimental result by 3.5% difference. Then we simulate the polymer flooding on the constructed digital rock and show that the simulation process is computational efficient. The proposed digital whole core construction workflow integrates the accuracy of LBM simulation and the efficiency of kriging method. The accurately constructed digital whole core only have 10143 blocks including inactive blocks, which greatly reduces the heavy computation time of chemical enhanced oil recovery simulation on digital rock and makes the proposed method more practical in routine analysis and modeling.

In CO 2 storage projects, CO 2 usually enters the target reservoir at a lower temperature than that of the surrounding rock and its density is increased. The injection temperature affects how much CO 2 can be stored. In this work we investigate the impact of heat exchange during CO 2 injection into the Surat Basin, Australia, using integrated reservoir modelling. We evaluate the aquifer storage and sealing capacities, as well as pressure build up and CO 2 plume migration. Flow simulations of CO 2 injection into the Precipice Sandstone were conducted with injection temperature from 40 to 80°C. The modelling domain consists of the reservoir sandstone, an overlying transition zone (muddy sandstone) and above it, the ultimate seal. The distribution of porosity, permeability and capillary pressure is heterogeneous. Heat exchange between rock and fluids was enabled in the commercial simulator to evaluate changes in fluid properties due to wellbore cooling. The initial temperature was set to 80°C. The injector’s wellbore pressure drop is modelled honouring a constant well head pressure of 15,000 kPa. The maximum allowed bottom-hole pressure is 90% of a thermally reduced fracturing pressure. The viscosity of water and CO 2 increases during cooling of the near wellbore zone; thus, pressure build up grows faster in the case of lower injection temperatures. Although the bottom-hole pressure becomes higher, injection rate is constrained by well head pressure. Heat exchange also increases the density and saturation of CO 2 at the plume edge, which causes a sharper and faster advancing front. Higher pressure in the reservoir forces fluids to migrate to the transition zone, which also reduces its temperature. CO 2 flows preferentially through the lowest capillary pressure channels and is able to permeate slightly into the transition zone. These physical conditions at the bottom of the well (lower temperature and higher pressure) lead to a denser CO 2 plume and a greater mass is stored in the reservoir each year. This work analyses non-isothermal injection of CO 2 into an aquifer using integrated reservoir modelling. It illustrates how reservoir cooling may increase the rate of CO 2 storage and slight migration to the transition zone.

As the oil and gas industry enters the digital era, openness is a key enabler to realizing the vision of transforming the industry for the better. The practice of reservoir engineering and reservoir simulation is no exception. In this paper, an openness mechanism in a reservoir simulator using Python scripting language is introduced. It empowers engineers to utilize simulation in new ways. It extends simulator capabilities and enables people to implement flexible-control logic to solve field management challenges. The new openness mechanism in the simulator allows engineers to program and include Python scripts in a simulation model. These scripts interrogate and interact with the simulator. The scripts are executed by the simulator while running the model. Flexibility is available to execute the scripts at every Newton iteration, before and after every simulation timestep, at specified times, or when a criterion is met. Simulation model properties can be queried through the scripts, such as well connections, well properties, group properties, grid region properties, network entities, current simulation time, etc. The scripts can set properties such as well constraints, well and connection productivity index (PI), group production target, pausing or stopping the run, etc. Customized control logic, if not directly available in the simulator, can be implemented in the scripts that interrogate and drive the simulator. Such customization can be packaged as Python libraries and shared with team members, enabling continuous value creation. Public Python libraries, such as NumPy, pandas and pywin32 or win32com, can also be loaded in the scripts to extend even further what the simulator can do. The openness mechanism is demonstrated on case examples. They include customized action to acidize wells when production drops, approximating the geomechanical effect of decreasing well pressure, modeling the effect of fines in injected water on well injectivity, and connecting to a network simulator. Examples are also given on customized reporting for model diagnostics and result interpretation, setting production constraints based on economics calculated in an Excel sheet with complex fiscal regime, advanced gas accounting, management of sulphur content, dual-optimization to meet gas demand while honoring oil treatment capacity, and integrated asset modeling from reservoir to surface networks to processing facilities. the ability to extend built-in functionalities of a reservoir simulator and customize field management controls using user scripting language. It embraces innovation and enables continuous value creation in reservoir simulation.

The faults in reservoir models have long been afflicted by ‘non-geological’ reservoir simulation practices. Many traditional production history matching workflows treat geological faults as ‘flood-gates’ that can be completely open, closed, or partially sealing as needed to match the production behavior observed at the wells. The predictive power of such models remains questionable. More realistic dynamic models consume a detailed fault seal analysis that utilizes fault displacement, juxtaposition, and clay content predictions to estimate properties that control flow across the faults. However, even when adopting this more geologically-sound approach, the mechanism of fault transmissibility multipliers (TMs) is used to incorporate the dynamic characteristics of faults in reservoir simulators. These TMs are not physical properties and instead represent a relationship between fault rock properties and host cell properties. Therefore, they only remain valid if the grid properties are not modified during history matching. Also, since a TM is not a physical property, rather a numerical simulation artefact, it is difficult to assign meaning and a reasonable range of uncertainty to it during history matching. This paper presents a new workflow for the assessment of uncertainty in fault flow dynamics. The workflow has been developed for a next-generation unstructured grid that uses cut cells to accurately honor structural and stratigraphic complexities. We describe the methods used to calculate side-dependent fault rock permeability and thickness estimates that are then directly provided as inputs to a next-generation reservoir simulator. The simulator incorporates the fault properties into the transmissibility calculation for cell-cell pairs separated by the fault. A scaling factor can be used to consistently scale fault rock properties if needed, and this is applied independently of the grid permeability. Example cases are presented to contrast the traditional approach with this improved approach. The potential shortcomings of the constant TM approach are exposed by examining the calibrated fault TMs for a history-matched case. We highlight the potential inconsistencies introduced in reservoir models with the use and subsequent manipulation of variable fault TMs. This inevitably leads to unphysical modifications to the spatial distribution of TMs during model calibration and a loss of geological insight. The implications on the reliability of production forecasts are discussed. A sensitivity study is conducted using the improved approach to quantify the relationship of the fault properties on flow performance. The benefits of the new methodology for the scaling of fault transmissibility during history match are demonstrated. This new approach provides the reservoir engineer with direct visibility of and control over geological fault rock properties in simulation models. This constrains the calibration of fault rock properties within a sensible uncertainty range, allows the grid permeability to be modified independently of the fault rock properties during history matching, and increases the reliability of production forecasts by preserving geological fidelity.

Chemical flooding is one of the classical EOR methods, together with thermal methods and gas injection. It is not a new method; indeed, the first polymer flood field pilots date back to the 1950s while the first surfactant-based pilots can be traced back to the 1960s. However, while both gas injection and thermal methods have long been recognised as field proven and are being used at a large scale in multiple fields, it is not the case for chemical EOR. Although there have been over 500 polymer flood pilots recorded, and almost 100 surfactant-based field tests, large scale field applications are few and far between. This situation seems to be evolving however, as more and more large scale chemical projects get underway. This paper proposes to review the status of chemical EOR worldwide to determine whether it is finally coming of age. The status of chemical EOR projects worldwide will be reviewed, focusing on recent and current large-scale field developments. This will allow to establish what is working and where the industry is still encountering difficulties. This review will cover North America, South America, Europe, the Middle East, Asia and Africa. It is clear that polymer flooding is now indeed becoming a well-established process, with many large-scale projects ongoing or in the early stages of implementation in particular in Canada, Argentina, India, Albania and Oman in addition to China. Strangely enough, the US lags behind with no ongoing large-scale polymer flood. The situation is more complex for surfactant-based processes. At the moment, large-scale projects can only be found in China and – although to a lesser extent – in Canada. The situation appears on the brink of changing however, with some large developments in the early stages in Oman, India and Russia. Still, the economics of surfactant-based processes are still challenging and there is some disagreement between the various actors as to whether surfactant-polymer or alkali-surfactant polymer is the way to go. This review will demonstrate that polymer flooding is now a mature technology that has finally made it to very large-scale field applications. Surfactant-based processes however, are lagging behind due in part to technical issues but even more to challenging economics. Still there is light at the end of the tunnel and the coming years may well be a turning point for this technology.

The first hydraulic-jet-fracturing multizone completion application was performed in an offshore high-permeability oil well located in the Bozhong oil field in Bohai Bay, China. A detailed case study is presented, highlighting operational challenges and lessons learned from the offshore multizone completion in the Bozhong field. Most offshore high-permeability oilwell completions in the Bozhong field employ tubing conveyed perforation (TCP) followed by frac-packing as multi-zone completions, which is practical in this field. Unlike conventional multizone frac-pack completions, to run perforation guns, the perforating must be completed first, followed by running isolation packers to complete the multizone frac-packing treatments. For hydraulic-jet-fracturing multizone completions, the hydraulic jetting tools are delivered to the target depth using drill pipe; following that, slurry fluid is pumped through the drill pipe to cut through the casing to create fluid flowing channels, which communicate with the pay zone formation. Lastly, the fracturing operation is completed by pumping both proppant slurry through the drill pipe and cleaning fluid through the annulus simultaneously, which creates dynamic hydraulic diversion as functions of isolation between zones. After completing one treatment zone, the tools are pulled to the next target depth, repeating the procedures, until the entire well is complete. After the fracturing treatment, the operator runs production tubing and other accessories, putting the well into production as usual. A detailed completion design and step-by-step operational procedures were prepared based on well data and work conditions to help ensure the effectiveness and safety of the offshore operation. Although some challenges were encountered during the operation, it was overall successfully performed, and significant performance improvement was achieved. After being in production for months, the candidate well has maintained a longer high-production-rate period than the offset wells completed using conventional multizone frac-pack completions. This hydraulic-jet-fracturing multizone completion has proven successful in the Bozhong oil field. The experiences and lessons learned can help benefit future multizone completion optimization for other similar offshore high-permeability oil wells.

The aim of this work is to study shale gas production subject to water blocking in compressible shale. Water blocking is a capillary pressure end-effect causing the wetting phase (e.g. water) to accumulate near the transition from a porous medium to an open medium; in this context, a transition from shale matrix to a hydraulic fracture. Shale is considered a tight porous medium with ultralow permeability, and hydraulic fracturing is essential to obtain economical production. Water is frequently used as a fracturing fluid, but its accumulation at the matrix end-face reduces the gas mobility and can lead to rapid decline of gas production rate. The tight nature of the shale as a porous medium also introduces non-standard flow and storage mechanisms. This work develops a mathematical model that accounts for apparent permeability, compressibility of gas and shale, gas adsorption, Forchheimer gas flow, and multiphase flow parameters like relative permeability and capillary pressure, which depend on wettability. The behavior of the model at steady state production is explored to understand the impact of the various mechanisms.

In tight shales, gas is stored in both free and adsorbed forms where the latter can make a significant or majority part of the gas in place. When the adsorbed layer thickness becomes of similar magnitude as the pore radius the adsorption can also affect the flow performance. We consider a 1D model for shale gas production where we implement adsorption with the adsorbed layer thickness as function of pressure, pore geometry where the pores are n -spherical (for 1 < n < 3). A higher n indicates more spherical pores, while a lower n means more fracture shaped pores. The shale is assumed to be compressible and its porosity and pore radius reduce with pressure depletion. The effective pore radius, which also depends on adsorption layer thickness, controls both intrinsic and apparent permeability. This study will address gas production by natural pressure depletion and the impact of the adsorption layer in the flow-compaction interplay. Marcellus shale data are used as input. For a given compressibility and hence porosity-pressure relation, the pore radius is less reduced at a high n than for a low n . The adsorbed layer thickness is assumed to be pressure dependent only, and fills a greater volume in the pores when the pores are more spherical (high n ). Increasing the maximum adsorption layer thickness makes the adsorbed layer fill more of the pore volume and gas in place. The increased volume fraction of adsorbed gas reduces the free gas saturation and the apparent permeability of the gas, resulting in delayed production compared to systems where the pores are less spherical (lower n ) and the adsorbed layer is thinner. Desorption is not very significant until pressure is reduced well below the initial value and mainly free gas is produced at early times. Hence, systems with more adsorbed volume fraction see lower recovery at a given pressure. Pressure depletion causes both the pore radius and the adsorbed layer to be reduced. The change in adsorbed layer with pressure is lower at high pressure and greater at low pressure, while pore radius changes more linearly with pressure. The free gas saturation can increase with pressure depletion (time) for low compressibility cases, or can reach a minimum before increasing in high compressible cases. It was observed that compressibility and production from the adsorbed layer both contributed significantly to recovery. Setting either the porosity or the adsorbed layer thickness constant (same as for the initial pressure condition) resulted in lower recoveries. Setting both constant gave the lowest recovery.

A detailed comparison is made of the more recently developed phenomenological 2-phase Modified Carman-Kozeny (2pMCK) relative permeability formulation with that of the industry standard, the Modified Brooks-Corey (MBC) formulation. The purpose is to show the strengths and weaknesses of the two formulations and to demonstrate how their combined use can yield the most consistent overall result, the optimum choice as input to reservoir simulation. A brief overview of the two relative permeability formulations is given first. Several laboratory data sets are reviewed by deploying the two models, validating the data and pinpointing potential problems. In some cases, both methods are used to extrapolate lab results to determine a more realistic residual oil saturation value and corresponding water relative permeability endpoint, particularly for samples involving fines movement. The apparent increased degree of curvature of the oil relative permeability is also often problematic and is typically related to pore-fill, requiring modification to laboratory defined relationships. A clear workflow is outlined on how to derive overall optimal results. Both methods show that if additional information such as independent wettability measurements are available, there is more confidence in final relationships derived. As evident, problems are typically associated with the second data point and the final one, start and finish of multi-phase flow measurements, necessitating adjustment in oil relative permeability curvature and the final endpoint data, residual oil saturation and associated water relative permeability. If the curvature of the oil relative permeability is excessive, MBC extrapolation is prone to failure. While the 2pMCK model does not show such shortcoming, the model cannot currently handle very large exponents. However, for most realistic situations, such limitation is not a problem. Another advantage of the 2pMCK model is its ability to pinpoint laboratory artefacts. The concurrent use of the two relative permeability formulations gives a new perspective of relative permeability modelling and is particularly suitable for quality checking and analysing more challenging laboratory results. The approach has been computerised, allowing for ease of data handling, model comparison and consistency.

Defining pressure dependent permeability (PDP) behaviour in coalbed methane (CBM) or coal seam gas (CSG) reservoirs using reservoir simulation is non-unique based on the uncertainty in coal properties and input parameters. A diagnostic fracture injection test (DFIT) can be used to investigate bulk permeability at a reservoir level and at lowered net effective stress conditions. As coal has minimal matrix porosity and under DFIT conditions cleat porosity is fluid saturated with reasonably definable total compressibility values, the DFIT data can provide insight into PDP parameters. At pressures above the fissure opening pressure, pressure dependent leak off (PDL) behaviour increases exponentially with increasing pressure. Many authors have noted that with decreasing pressure PDP declines exponentially with increasing net effective stress. Thus, PDP behaviour can be defined by PDL. In this paper, we show how combined analyses, using typically collected field data, can be used to better define and constrain the modelling of PDP. We illustrate this process based on a well case study that includes the following data: fracture fabric and porosity reasonably defined from image log and areal core studies; DFIT data acquired under initial saturation conditions; hydraulic fracturing data; and longer term production data. These analyses will be integrated and used to constrain the parameters required to obtain a rate and pressure history-match from the post-frac well production data. This workflow has application in other coal seam gas cases by identifying key variables where hydraulic fracturing performance has been unable to overcome limitations based on pressure or stress dependent behaviours and often accompanied by low reservoir permeability values. While this is purposely targeting areas where only typically collected field data is available, this workflow can include coal testing data for matrix swelling/shrinkage properties or other production data analysis techniques.

When flow is initiated from a well, a physical shock front is created in the reservoir, beginning at the completion. This shock front moves out into the reservoir as a Mechanical Wave with constant intensity and with velocity decreasing vs. the square of the distance. This is known as infinite acting radial flow (aka ‘transient flow’) and is very well understood (at least at the producing well’s location). The capillary shockwave represents a moving boundary to the pressure decay field that forms around the well ( Hurst 1934 ). This active region of decay behind the moving shock front will adopt a near semi-steady state flow regime in the reservoir, as required by the second law of thermodynamics. This depletion region is composed of radial capillaries that produce flow to the well and create pressure or elastic depletion in the reservoir. The cone of influence or region of pressure depletion bounded by the shock front around the well is the active reservoir at any given time. Eventually, the capillary shockwave boundary condition at the radius of investigation will either encounter all of the reservoir boundaries, or will continue to grow into an aquifer. Energy is redistributed by a faster moving inertial wave that passes through the open capillaries. Each capillary has finite rupture strength due to the same initiating capillary pressure that produces the bounding shockwave ( Goldsberry 1998 , 2000 ). Hence the cone of influence can be recognized as a structure composed of radiating capillaries behind a moving boundary condition. Each capillary is considered an individual contributor to the flow to the well and each capillary has a contribution to flow that is in proportion to its volume ( Goldsberry 1998 ). When a capillary reaches a sealing boundary, it ceases to grow and contributes less to the flow of the well than the capillaries that are still growing. Since the well is generally controlled by a choke, the demand upon the cone of influence is constant. This creates an imbalance in the second law of thermodynamics. In order to reconcile this imbalance, the choke-well interaction then produces a secondary cone of influence bounded by its own capillary shockwave boundary condition. The goal of this paper is to describe the primary shockwave boundary condition and the inertial waves that produce pressure redistribution within the cone of influence. Traditionally, the inertial term is ignored in order to create a simplified diffusion equation model. It is not zero. Furthermore, in the classic diffusivity derivation ( Earlougher 1977 ), the capillary entry pressure is assumed to be small and hence neglected. It is also not zero. By excluding these terms in the classic derivation, the result is a simplified model that works for infinite acting radial flow at the producing well location, but does not match observation well data, nor producing well data after the first boundary is encountered. If both inertia and capillary entry/threshold pressure are included in the derivation for the diffusivity equation for transient flow, a model that is based upon a capillary structure emerges, which predicts (and matches real field data) a depletion region that grows predictably with time, with distinct boundary contacts and arrivals of primary and secondary shock fronts.

Fines migration causes significant formation damage during oil and gas production. The reliable prediction of well performance and extend of the damaged zone allow for planning and design of well stimulation for prevention, mitigation and removal of the damage. We derive a novel analytical model for production well performance during fines migration. The model includes explicit formulae for concentrations of suspended and strained fines, and the expressions for skin factor and well impedance. Ten field cases exhibit close match with the analytical model. Moreover, the tuned parameters belong to common intervals of their variation. The work provides means for reliable well behaviour prediction based on production history and coreflood data.

Condensate banking is one of the main challenges facing operators of gas condensate reservoirs. Different techniques have been used over the years to improve well deliverability in condensate reservoirs, including gas recycling, gas injection, matrix acidization and hydraulic fracturing. This study investigates design options for effective CO 2 injection strategies to reduce liquid blockage and improve productivity in tight reservoirs. We use dynamic compositional models to simulate condensate formation under realistic operating conditions. Initial sensitivity studies are performed using single well models, to design simulation cases that capture the condensate banking problem accurately for selected permeability scenarios. Then full field models are constructed to evaluate the potential of CO 2 injection schemes: near-wellbore huff and puff, and field scale continuous injection. Different field development options are investigated, considering the effects of well placement, injected gas volumes, quality of injected gas and frequency of huff and puff cycles. The results of this work show that CO 2 huff and puff processes can be highly effective for mitigating condensate drop-out in tight reservoirs, whereas there is likely to be no significant advantage for huff and puff in comparison to continuous CO 2 injection in moderately permeable reservoirs. Well spacing and injected volumes can impact recovery significantly: we suggest analytic methods, based on well drainage areas, to estimate suitable values for these parameters. As the production scheme will involve recycling CO 2 , we consider the effect of impurities in the injection stream: our study indicates that it is not essential to use pure CO 2 but sensitivity studies should be performed to check the effects of injected gas composition on recovery. We also demonstrate that recovery from huff and puff operations can be improved by using variable-length cycles, and propose a method to adjust the injection periods dynamically. In this paper, we have identified important parameters that can affect the performance of CO 2 huff and puff processes in tight gas condensate fields. The methodology described here provides a guideline for simulation studies of CO 2 injection, which will help reservoir engineers to optimize field development and operations.