Bati Raman field, with an original oil in place of 1.85 billion barrels, is a naturally fractured carbonate reservoir containing 9-13 °API extra heavy oil with viscosities varying from 300 to 600 cp. Not only a wide range of pilot EOR schemes including gas, thermal and chemical methods, but also novel IOR applications have been tried in the field. CO2 injection was a game changer for this reservoir which has been the main drive mechanism since 1987. Since then, various techniques are applied to further improve the production performance of the field. This study focuses on the design and outcome of the pilot acid fracturing treatments in selected three wells in the tighter and less fractured southeastern part of the reservoir. State of the art planning included full evaluation of well integrity, cement bond and open hole logs, geomechanics studies augmented with rock mechanics laboratory tests. Laboratory tests were also conducted focusing on sludge/emulsion forming tendencies and acid reaction rates. Using these results, expected fracture dimensions were predicted along with production forecasts. In all wells, pre-frac calibration tests were conducted to assess stress conditions and fracturing parameter optimization. The treatments were then executed, improving the procedure between each well for acid fracturing. Injections schemes were operationally efficient and various diversion techniques were used to mitigate the presence of naturally fractured zones. Pre and post-job temperature logs helped to evaluate each treatment. The results from the wells were very positive; total production rate increased about fivefold, observed within one month after the treatments. No considerable change in water or CO2 production in the wells was observed which had been one of the most important objectives during the candidate selection process. One well was suspended, which turned out to be one of the producers of the field after acid fracturing treatment. Each well had a different post-frac production performance because of its geological characteristics and flow dynamics, making the study more valuable for better understanding of the process. The wells are still on critical observation to assess the nature of the created fractures and their longevity in the long run. Even after twelve to fifteen months of production, which is the breaking point period for fracture closure, the overall production level of the wells was double compared to pre-frac rates. One well still has a fracture dominated production while other two changed back into its pre-frac rates. Based on these results, acid fracturing campaign was extended in the area which is currently under evaluation.

This paper presents a fit-for-purpose gas well performance model that utilizes a minimum set of inflow and outflow performance parameters, and demonstrates the use of this model to describe real-time well performance, to compare well performance over time and between wells, and to generate production forecasts in support of well interventions. The inflow and outflow parameters are directly related to well-known reservoir and well properties, and can be calibrated against common well surveillance and production data. By adopting this approach, engineers develop a better appreciation of the magnitude and uncertainty of gas well and reservoir performance parameters.

This paper presents an analysis of the stimulation treatment design and the optimization of the stimulation treatments in the challenging ultra-HPHT environment (400°F and 15000psi) of the Krishna-Godavari (KG) basin, East Coast, India. In greater detail, the paper focuses on how the perforation placement and the stimulation treatment design have been optimized for each zone of a multi-zone treatment program in a deviated well to address the specific challenges (geological, completion, operational and logistical) associated with the environment. An in-depth analysis was performed on the stimulation design prior to mobilization of stimulation equipment and crew. The treatments were designed by utilizing as inputs tailored petrophysical and geomechanical models, well design data, stimulation material properties and equipment capacities, and analysis of prior stimulation experience in this area. Extensive sensitivity analysis was carried out to come up with the optimum perforation depths and stimulation treatment. Subsequently, when on location, the treatment design was fine-tuned using real-time data to optimally place the fracture. The overall goal was to determine the best stimulation treatment for the offshore well without inducing negatively impacting either the reservoir production or ultimate recovery. From adherence to fundamentals of well and frac design to completion optimization, major efforts were made in the treatment optimization for each zone based on the challenges associated with the KG basin. These challenges, in no particular order, include high temperature and high pressure, proximity to water zones and the necessity to isolate treatment stages using unconventional methods, presence of high fluid loss zones, and logistical/space constraints inherent from the offshore location. Stimulation treatments implemented in this field in the past were analyzed to better understand the pros and cons of the various stimulation techniques and practices that were employed. A key learning from this exercise was that the stimulation fluid selection is of utmost importance. With a BHST of 400°F, stimulation fluids that can provide adequate stability under these conditions are limited. This led to an increased focus on the engineering design of stimulation treatments in the pre-planning phase, which was then optimized as real-time data was acquired. Wellbore re-entry issues led to further re-evaluation and redesign of the perforation strategy. Improvements in treatment sizing were made during the stimulation as water zones needed to be avoided or stress conditions needed to be corrected from early design conditions assumed. Furthermore, upon completion of each stimulation stage, proper and unconventional isolation methods were needed from earlier stimulation due to tubular limitations. Post-frac evaluation using hydraulic fracture pressure match indicated that 3 out of the 5 zones were stimulated with highly conductive and long fractures, while minimal size treatments were placed in 2 troublesome zones. Also, treating a high-risk water zone was avoided. In conclusion, the authors believe that the stimulation program was optimally designed and conducted in an area with limited success in years past by using sound engineering in all the phases of the design and implementation.

This paper will focus on a new system for separation of water in downhole horizontal wells. The advantages with the system are related to the fact that the water produced from the well is not lifted to the surface, but re-injected into suitable parts of the reservoir, either for pressure support or for diposal. The method of water separation and re-injection has been evaluated for oil producing fields. The paper presents details of the technical solutions and analysis done related to the financial analysis/payback. The mechanical design is basically a main pipe section of a few meters of length, with a special geometry utilizing gravity-based separation. A technical and economic analysis of a downhole processing plant (DPP) using a horizontally installed water/oil separator has been performed. The Improved Oil Recovery (IOR)part has been analysed with a relevant flow simulation tool. Based on the given reservoir depth/pressure, flow rate, viscosity/density and water cut, the simulations show that a significant improved production rate/income can be achieved by extracting the produced water downhole and performing re-injection into the producing reservoir to maintain reservoir pressure. In addition, the expected lifetime of the well is increased by several years. The conclusion is that the earlier the separator is installed, the greater the total well income. In addition, details regarding not only multi-lateral wells through level 5 junctions but also production string with separator and valve system has been evaluated and is concluded to be feasible for the well in question The removal of water downhole has several advantages, for example the removal of the water column up to the surface will reduce the reservoir back pressure and will improve recovery /production rates. In addition, not lifting the water will reduce energy consumption/CO 2 footprint, and removal of water will reduce surface processing and possible re-injection and chemical treatment cost. In general, water separation downhole is advantageous, due to the higher pressure.

With the decline of conventional resources, the tight oil reserves in the Daqing oilfield are becoming increasingly important, but measuring relative permeability and determining production forecasts through laboratory core flow tests for tight formations are both difficult and time consuming. Results of laboratory testing are questionable due to the very small pore volume and low permeability of the reservoir rock, and there are challenges in controlling critical parameters during the special core analysis (SCAL) tests. In this paper, the protocol and workflow of a digital rock study for tight sandstones of the Daqing oilfield are discussed. The workflow includes 1) using a combination of various imaging methods to build rock models that are representative of reservoir rocks, 2) constructing digital fluid models of reservoir fluids and injectants, 3) applying laboratory measured wettability index data to define rock-fluid interaction in digital rock models, 4) performing pore-scale modelling to accelerate reservoir characterization and reduce the uncertainty, and 5) performing digital enhanced oil recovery (EOR) tests to analyze potential benefits of different scenarios. The target formations are tight (0.01 to 5 md range) sandstones that have a combination of large grain sizes juxtaposed against small pore openings which makes it challenging to select an appropriate set of imaging tools. To overcome the wide range of pore and grain scales, the imaging tools selected for the study included high resolution microCT imaging on core plugs and mini-plugs sampled from original plugs, overview scanning electron microscopy (SEM) imaging, mineralogical mapping, and high-resolution SEM imaging on the mini-plugs. High resolution digital rock models were constructed and subsequently upscaled to the plug level to differentiate bedding and other features could be differentiated. Permeability and porosity of digital rock models were benchmarked against laboratory measurements to confirm representativeness. The laboratory measured Amott-Harvey wettability index of restored core plugs was honored and applied to the digital rock models. Digital fluid models were built using the fluid PVT data. A Direct HydroDynamic (DHD) pore-scale flow simulator based on density functional hydrodynamics was used to model multiphase flow in the digital experiments. Capillary pressure, water-oil, surfactant solution-oil, and CO 2 -oil relative permeability were computed, as well as primary depletion followed with three-cycle CO 2 huff-n-puff, and primary depletion followed with three-cycle surfactant solution huff-n-puff processes. Recovery factors were obtained for each method and resulting values were compared to establish most effective field development scenarios. The workflow developed in this paper provides fast and reliable means of obtaining critical data for field development design. Capillary pressure and relative permeability data obtained through digital experiments provide key input parameters for reservoir simulation; production scenario forecasts help evaluate various EOR methods. Digital simulations allow the different scenarios to be run on identical rock samples numerous times, which is not possible in the laboratory.

Approximately 40% of unconventional wells in the United States with artificial lift are using gas lift. One of the blessings of gas lift is that it is very forgiving and rarely fails. This has led some operators to the conclusion that gas lift doesn’t require a proper control and optimization scheme. The objective of this study is to use field data and show the importance and benefit of a controlled and optimized gas lift compressor to aid production. The intelligent gas lift compressor utilizes a heavy computational process of taking a composite Vogel reservoir inflow model coupled with a Hagedorn & Brown outflow model that informs the Guo model to solve for a minimum critical rate to lift liquid droplets out of the wellbore. First, an empirical range of injection rates is used to find the rate with maximum unloading as indicated by a drop in casing injection pressure. Once this rate is found, reservoir inflow performance relationship (IPR) parameters are estimated to meet that condition. These parameters are held constant for a time period and injection rate needed is calculated based on production rate and pressure coming into the programmable logic controller (PLC) via digital connections. This approach is tested on a well and the production is monitored over a 3-month period. The collected data are used to analyze the benefits of an intelligent gas lift compressor control. The data from a well with this intelligent gas lift system is monitored over a 3-month period. This includes a 20-day period of the compressor searching for an injection rate that shows to be the most efficient. Then the compressor control is set up with a critical rate control mode. This calculation is performed with an edge server that runs the intense calculations every minute to instruct the compressor what volume of injection it needs to achieve manipulation of an automated suction control valve and speed control of the engine driver. The surface casing pressure data shows a very stable unloading behavior profile, which is confirmed with very stable oil and gas production data over the observation period. An estimate of gained production during the periods of high line pressure along with stable unloading is given as the justification for outfitting the well site with the intelligent compressor controls. Despite gas lift’s importance as a lift technique, its control and optimization are still not properly addressed within the industry. This work’s proposed intelligent gas lift scheme can be a potentially valuable solution to unstable unloading of liquids, and benefit the operators significantly in production and revenues.

The authors have developed an algorithm that combines simple, proven, analytical work tools. The algorithm allows one to quickly update the potential, taking into account geological and technical factors, assess risks, estimate current recoverable reserves, incremental oil production, and economics for each well. The purpose of the work is to reduce routine labor costs when searching for and selecting wells, to rank the entire list of relevant candidates, to increase the share of successful operations, as well as to increase incremental oil production by accelerating well optimization with minimal risk. A pilot project helped to improve the success rate of optimization jobs from 75 to 89%, and increase specific incremental oil production by 57% in comparison with the previous year. The tool can also be used for auditing decisions as a retrospective.

Casabe is a multilayer field (20 reservoirs distributed in 3 formations) characterized high heterogeneous nature, limited sand continuity, unfavorable mobility, and high sediment production due to a poor consolidation of the reservoir. The ongoing waterflooding process has a well defined five-spot pattern where injection wells have selective strings installed, and production is commingled. Under these conditions, shut-in wells for reservoir pressure acquisition are challenging activities with high operational risks related to compromised mechanical integrity and sediment production. Likewise, the commingled production scheme affects the deconvolute pressure data process and promotes crossflow effects leading to inconsistent results. Finally, stoping the well involves production loss and disruption in waterflooding balance. The reservoir pressure is a critical requirement to run an analytic optimization methodology implemented in 2017. Casabe had proven that selective injection string offers a unique opportunity to acquire quality reservoir pressure data by reservoir unit using the Downhole Dummy Pressure Sensors (DDPS), overcoming all the above issues when acquired in producers. This paper presents how a waterflooding optimization methodology coupled with a quality reservoir pressure acquisition enhanced reservoir surveillance by triggering actions to optimize the waterflooding volumetric balance, thus, reaching a production-injection system stabilization. This enhancement changed the area production decline trend and allowed to contact more zones and produce oil from constrained layers.

Objectives/Scope This field case history details the objectives, design, field operations, and production, pressure, and other surveillance results of the first rich gas multiwell cyclic huff ‘n’ puff pilot in the Bakken and Three Forks intervals of the Williston Basin. The broad goal of the enhanced oil recovery (EOR) pilot was to identify key performance metrics of rich gas injection leading to the design of a commercial field-scale EOR process. Objectives included demonstrating the ability to contain gas within the target intervals vertically and laterally and build pressure to promote a miscible displacement process in a fully developed 1280-acre drill spacing unit (DSU) in the Bakken tight oil play. Methods, Procedures, Process The paper includes the geologic reservoir description of the pilot area in the northeast Williams County Nesson Anticline area based on well logs, seismic, and core materials. Background includes the development history of the eleven-well, 1280-acre DSU including well completions and production data. The paper describes details on methods and results of laboratory studies of representative Bakken fluids and core materials including characterizing PVT (pressure, volume, temperature) properties, minimum miscibility pressure (MMP) measurements, and fluid extraction tests. Case results include a description of the facilities and field operations through the execution of the pilot. Further, the paper includes key considerations in the planning and design phase, including injection order, offset monitoring, and facilities design considerations for gas rates and volumes. Production results from within the DSU and surrounding DSUs include a total of 24 horizontal Bakken or Three Forks wells. Surveillance results include oil, water, and gas rates; injection and downhole pressures; and gas chromatograph data from injection and offset wells as a basis for assessing pilot results versus goals and objectives. Results, Observations, Conclusions Bakken/Three Forks produced gas is miscible with reservoir fluids at pressures above ~2500 psi, given the relatively high fluid fraction of ethane and propane. This produced gas recovers a large fraction of reservoir fluids from Middle Bakken, Three Forks, and Upper and Lower Bakken Shales in laboratory extraction tests. Gas injection into the Bakken and Three Forks intervals was achieved through the full range of the pilot design, and over 90% of the injection gas was recovered as wells were returned to production based on measured rates, volumes, and other surveillance data. While actual pilot gas injection rates were too low to achieve a material EOR oil response, surveillance data indicated that pressures increased with gas injection and that gas was contained within the 1280-acre DSU as designed. Further, history-matched simulations indicated that higher gas injection rates could yield EOR recoveries comparable to that reported in successful Eagle Ford projects. Key insights include that much higher gas injection rates are required for an economical process and that initiation of gas injection cycles earlier in the well life will reduce the volume of gas and injection time required to build bottomhole pressures above the MMP to promote EOR. Novel/Additive Information This first multiwell rich gas injection pilot in the Bakken/Three Forks play identified several design and operational efficiencies beneficial to an economical field-scale project. For example, jet pump installations allowed gas injection operations without modifying production wells via costly workovers. Injection/production conversions could be completed at minimal cost by cycling out jet pumps and installing pressure gauges. Further, wells completed with cemented plug and perforated liners for hydraulic fracture stimulation may promote improved gas injection conformance across the horizontal completed intervals.

The artificial lift strategy described in this document addresses new challenges resulting from an accelerated implementation of secondary recovery in a mature oilfield. New technologies and best operational practices were implemented to handle challenges such as high pressure and water flowrates in source water wells, extended-life ESP to enable production across a wide production range to cover the response of the waterflooding, increasing solids production, heavy oil, and installations of ESP in deep and tortuous wells. This technical and strategic approach covers artificial lift designs for water injection in well-to-well configurations located in the same platform. This approach reduces the requirements for surface facilities. For oil producers, ESP robustness must be enhanced to handle wide range and aggressive reservoir conditions. Slim ESP applications to allow taking full advantage of waterflooding. A novel concept applied to recirculate hot water making possible produce deep heavy oil reservoirs and updated workflows and equipment for remote surveillance of the ESP systems and reservoir. The integrated approach of the strategy allows its application in either brownfield or greenfield. Putting such strategy into action resulted in an incremental production of 30,000 bopd from waterflooding using more than 60 ESP installations of wide range and reducing workover interventions representing savings of 4.5 MMUSD. Additionally, direct well-to-well injection in 22 water producer wells allowed direct injection of 100,000 bwpd at roughly 2,000 psi at the well head and savings of 14 MMUSD in surface facilities.

A fundamental step towards accurate reservoir characterization and hydrocarbon in-place estimation involves correct fluid type identification. Precise modeling is a challenge for gas condensates due to their complex near-critical behavior. Moreover, this becomes an uphill task when commingled production from a compartmentalized reservoir is going on. This work presents a data-driven workflow to solve the fluid type identification challenge, for a compartmentalized and layered field that was previously characterized as having shallow gas condensate and deeper oil layers. Consequently, a single, robust and coherent EOS model was obtained to successfully predict field-wide hydrocarbon properties. Fluid analysis of seven different separator samples of five formations from three wells were analyzed in this study. The development and implementation of the new workflow includes multiple stages. An extensive data analysis is carried out, which includes compositional comparative analysis of the samples, comparison between lab and producing gas-oil-ratios, and establishment of compositional grading utilizing key PVT parameters. Next, a decision tree methodology was utilized to prioritize samples based on lab adjustments along with saturation pressure and reservoir pressure comparison and apply the compositional gradient approach on the most representative dataset. Finally, a common EOS model for the field was developed, tuned, and validated through quality checks by a simulation model. Comparative compositional analysis helped in rectifying PVT lab's fluid classification for one of the samples based on C7+ composition. Evidence of compositional variation with depth is observed; an increase in C7+ percentage and reduction in API shows heavier components in the fluid column from top to bottom. The high-priority data points exhibited a gradient without contact (representative of near-critical fluids), in agreement with the comparative analysis. Furthermore, about 30% liquid dropout for a pressure change of 200-300 psi for the oil sample is also pointing towards a near-critical system. The compositional gradient modeling enabled a single coherent solution to be applied, which accurately predicts both oil and gas properties. Finally, the EOS model was tested and validated through a simulation model-based workflow whose results are consistent with the measured lab parameters. This final test was important, as there was always the alternative hypothesis: a gas-condensate underlain by a volatile oil with a distinct GOC. Some of the data points were consistent with this hypothesis, and it was not immediately obvious as to which type of fluid existed in the reservoir. Other evidence included unusual GOR versus time behavior, which was inconsistent with a sharp GOC. The resulting PVT model enabled the prediction of gas vs. condensate fluids field-wide for a highly complex fluid system. An attempt to unravel the enigma through integrated data analysis has helped in providing a new perspective of near-critical fluid system for field.

Using commercial numerical reservoir simulators to build a full field reservoir model and simultaneously history match multiple dynamic variables for a highly complex, offshore mature field in Malaysia, had proven to be challenging, manpower intensive, highly expensive, and not very successful. This field includes almost two hundred wells that have been completed in more than 60 different, non-continuous reservoir layers. The field has been producing oil, gas and water for decades. The objective of this article is to demonstrate how Artificial Intelligence (AI) and Machine Learning is used to build a purely data-driven reservoir simulation model that successfully history match all the dynamic variables for all the wells in this field and subsequently used for production forecast. The model has been validated in space and time. The AI and Machine Learning technology that was used to build the dynamic reservoir simulation and modeling is called spatio-temporal learning. Spatio-temporal learning is a machine-learning algorithm specifically developed for data-driven modeling of the physics of fluid flow through porous media. Spatio-temporal learning is used in the context of Deconvolutional Neural Networks. In this article Spatio-temporal Learning and Deconvolutional Neural Networks will be explained. This new technology is the result of more than 20 years of research and development in the application of AI and Machine Learning in reservoir modeling. This technology develops a coupled reservoir and wellbore model that for this particular oil & gas field in Malaysia uses choke setting, well-head pressure and well-head temperature as input and simultaneously history matches Oil production, GOR, WC, reservoir pressure, and water saturation for more than a hundred wells through a completely automated process. Once the data-driven reservoir model is developed and history matched, it is blind validated in space and time in order to establish a reliable and robust reservoir model to be used for decision making purposes and opportunity generation to maximise the field value. The concepts and the methodology of history match of multiple wells, individual offshore platforms, and the entire field will be presented in this article along with the results of blind validation and production forecasting. Results of using this model to perform uncertainty quantification will also be presented. A case study of a highly complex mature field with large number of wells and years of production has been used to be studied and simulated by this data-driven approach. Time, efforts, and resources required for the development of the dynamic reservoir simulation models using AI and Machine Learning is considerably less than time and resources required using the commercial numerical simulators. It is validated that the TDM developed model can make very reasonable prediction of field behavior and production from the existing wells based on modification of operational constraints and can be a prudent complementary tool to conventional numerical simulators for such complex assets.

Asset management success is accomplished when the integrated production system is operating close to its intended potential. Continuous awareness of wells and facility conditions are key factor in the realization of designed capacity. In contrast, unknown status and conditions can severely limit production capacity. The rise of instrumentation technologies over the last four decades have created new opportunities to understand well and reservoir behavior. However, despite of being proved as a cost-effective surveillance initiative, remote monitoring is still not adopted in over 60% of oil and gas fields around the world. Understanding the value of data through machine learning techniques is the basis for establishing a robust surveillance strategy. The objective of this paper is to develop a data-driven approach, enabled by Artificial Intelligence (AI) methodologies including machine learning (ML), to find optimal operating envelope for gas-lift wells. The process involves building ML models for generating instantaneous predictions of multiphase flow rates and other quantities of interest, such as GOR, WCT, using real-time sensor data at the surface, historical performance, and sporadic test data. Additionally, forecasting models were developed for generating short-term (30 days) forecast of cumulative oil, water, gas, and liquid production, multiphase flow rates, WCT, GOR, and reservoir pressure. Using time-series forecasting models, a sensitivity analysis was performed to generate short-term well response for a selected number of combinations of choke settings, and gas injection rates. Sensitivity analysis provides 2D maps of well response highlight an operating envelope, which are proposed to be combined with physical and operational constraints to arrive at optimal operating conditions, which may effortlessly add 2.5% net profit from optimum gas-lift alocation. The results of this work show encouraging results, and demonstrate value that AI-enabled methodologies can provide in instrumented wells by enabling automated workflows for virtual metering, production allocation, short-term production forecasting, and deriving optimal operating conditions. The developed AI methodology has tremendous potential of integration in an end-to-end workflow of autonomous well control by utilizing available data to produce easy to update ML models, with little to no human intervention.

Objectives/Scope This paper uses pseudo-time to extend the application of constrained multiwell deconvolution algorithm to gas reservoirs with significant pressure depletion. Multiwell deconvolution is the extension of single well deconvolution to multiple interfering wells. Constraints are added to account for a-priori knowledge on the expected deconvolved derivative behaviors and to eliminate non-physical solutions. Methods, Procedures, Process Multiwell deconvolution converts pressure and rate histories from interfering wells into constant-rate pressure responses for each well as if it were producing alone in the reservoir. It also extracts the interference responses observed at each of the other wells due to this single well production. The deconvolved responses have the same duration as the pressure history. This allows to identify reservoir features not visible during individual build ups. Deconvolution techniques can only be applied to pressure and rate data when flow can be represented by linear equations. In strongly depleted gas reservoirs, fluid properties, and gas compressibility in particular, are pressure dependent, which makes the flow problem non-linear. The paper uses pseudo-pressure and pseudo-time transforms to linearize the problem in such conditions. Results, Observations, Conclusions The pseudo-time method developed by Levitan and Wilson (2010) is extended to constrained multiwell deconvolution in highly depleted gas reservoirs. It is an iterative process, which yields the correct initial gas in place, whereas the use of real time does not. The algorithm is validated with a two gas well synthetic example, and successfully applied to a field case with four gas producers. Novel/Additive Information The paper extends the application of constrained multiwell deconvolution to strongly depleted gas reservoirs. Constrained multiwell deconvolution is an efficient way to exploit data recorded by permanent downhole pressure gauges and provides information not otherwise available. It can help to identify field heterogeneities and compartmentalization early in field life, making it possible to modify the field development plan and to improve locations of future wells. It can accelerate history-matching with the reservoir model by doing it on the constant rate pressure responses rather than on the actual, usually complex, production history. An added advantage is that comparison between the pressure derivatives of the model and the actual deconvolved derivatives allows identification of mismatch causes.

This paper presents a simple yet rigorous analytical solution for two-phase (gas-oil) flow in closed volatile oil reservoirs. The solution includes all flow regimes over the life of a multi-fractured horizontal well, including the usually long-duration early transient flow followed by the transition and the boundary-dominated flow regimes. The solution will be particularly useful in rate transient analysis of production data and production forecasting for horizontal wells with multiple fractures in ultra-low permeability reservoirs, such as shales. We formulated the governing, non-linear partial differential equations (PDEs) for simultaneous gas-oil flow with an inner boundary condition of constant bottom-hole pressure (BHP). We then defined pseudo-variables to transform the non-linear PDEs to linear forms. By developing deterministic models for calculation of fluid properties using multi-regression analysis of PVT data and relative permeability curves, we were able to find analytical solutions by the separation of variables method for specified initial and outer boundary conditions. We obtained a production rate-time relation which can be used to generate type curves or to provide a basis for history matching production data and forecasting future production. Under constant bottom-hole pressure producing condition, the resulting solutions that describe the relationship between dimensionless rate and dimensionless two-phase pseudotime indicate a complicated decline with an exponential relation inside an infinite series. We validated the solutions through comparisons with compositional simulation using commercial software; the satisfactory agreements demonstrated the accuracy and utility of the analytical solutions. Our results indicate that the production performance in multi-phase flow is far different than performance in single-phase flow, and that formation properties interpreted using techniques appropriate for single-phase flow can be seriously in error when applied to two-phase flow situations. Finally, we found that our analytical solution yielded reasonable interpretations of actual field data from the Midland Basin.

This paper reviews the U.S. shale well drilling and completion strategies in recent years. A data-driven approach is taken to correlate the production performance to the lateral length focusing on the Midland basin and Delaware basin. Six lateral length cases were studied, ranging from 5 thousand to 17.5 thousand feet. To investigate the shale well lateral length impact on production performance, multi-disciplinary studies were conducted by taking into account the combinations of time-dependent fluid properties, well trajectory, reservoir characteristics, completion design, and wellbore multiphase flow effect. Artificial lift and operational strategies were discussed and developed for long lateral length wells. Economics analysis was performed to study the feasibility and sustainability of production operation strategies.

After a successful decade of exploration and development activities in major tight/shale reservoirs, the industy now has access to incredible sets of data, modeling tools, and technologies for multi-fractured horizontal well (MFHW) completion. A review of the available data and models shows that performance of a MFHW is governed by hydraulic facture properties (dimension, conductivity, and distribution) and reservoir fluid and rock characteristics (reservoir fluid properties, and rock storage and flow capacities). Workflows are required to link the characterization attempts (reservoir and MFHW), learnings from completion expriments, modeling approaches (reservoir and fracture modeing) and pettern recognition exercises (relationship between well performance metrics and the governing parameters). In the current study, an interative workflow is proposed for design and optimization of MFHW completion based on a mixed-method approach combining three major paradigms: experiments, modeling, and data science. Each cycle of the workflow starts with data gathering and characterization of reservoir fluid and rock, followed by reservoir and fracture modeling, statistical analysis, updated design, economc analysis, and ends with implementation, monitoting and data analysis. The first cycle of the workflow is the most time-consuming and tedious one which requires a great deal of discussions and instructions. The proposed workflow is tried on a population of Montney gas condensate wells. Rate-transient analysis (RTA) and numerical reservoir modeling were applied to a group of 16 Monteny gas condensate wells with detailed daily production and flowing pressure data. Further, a simplified RTA-based approach and statistical analysis were applied to more than 90 Montney gas condensate wells (from the same region) with publically available production data. A new design with optimized completion paramteres is obtained from the results of RTA, numerical reservoir modeling, statistical and ecnomic analyses. The new design is applied to six new wells in the same area. The average performance of the new wells is reasonably close to the predicted performance by the proposed workflow. The workflow is believed to optimize the well performance, save the operator millions of dollars through optimization, and give the management and technical teams confidence in the next phase of corporate planning.

Analysis of production perspectives for very deep wells (below 20,000 ft true vertical depth) is closely related to understanding of the hydraulic fracturing perspectives because at these depths, the overburden stresses and long geological history of the target formations usually result in low permeability. Costs of hydraulic fracturing operations in this type of well is significantly higher than in shallower conditions; however, the risk of nonprofitable outcome is significant. A workflow, including several key methods, is suggested to mitigate the operational and financial risks of potential hydraulic fracturing operations in very deep environments. These techniques employ cost-effective yet technically strong tools for determination of required reservoir parameters and well information, including fracture closure pressure, reservoir pressure, formation transmissibility, and well tubulars integrity. Suggested methods avoid well intervention and associated rig and services costs. The workflow was applied in a very deep and hot well with target intervals below 21,000 ft and helped in estimating the fracturing perspectives in an efficient manner.

The traditional Material Balance Analysis only utilizes cumulative production and static average reservoir pressure. Time does not feature in the equations. However, there are significant advantages if time is incorporated into the analysis. For example: a) identifying if all the wells belong to the same reservoir; b) identifying the effect of external energy sources such as gas or water drive; c) incorporating the contribution of communicating tight reservoirs; d) visualization of the results in pressure-time format. The time-based analysis presented in this paper supplements the conventional methods. It helps reduce the non-uniqueness of the solution. In contrast to the conventional Havlena-Odeh plotting variables, which are complex and non-intuitive, the pressure-time plot and corresponding pressure-history match are much easier for an engineer to comprehend and to evaluate the validity or uniqueness of the results.

During the last five years, the use of permanent downhole gauges has proliferated in the industry. The availability of true bottomhole pressure (BHP) is imperative in validating/improving reservoir models. Similarly to the extrapolation of BHP from surface readings, the use of BHP to extrapolate formation pressure may lead to significant errors in reservoir models that do not provide operators with the competitive edge needed in the current market. Consequently, there is a drive to monitor formation pressure in-situ by placing pressure and temperature gauges in direct contact with the formation. In recent years, operators have been drilling larger holes, deploying gauge systems on the exterior of the casing, and cementing the gauge systems in place for multiple purposes. In artificial lift applications, cemented gauge systems have helped operators to avoid costs of decompleting and redeploying gauge systems on tubing whenever the electric submersible pumps (ESP) must be serviced, or perhaps whenever operators want to convert an observation well to a producing well. In unconventional plays, technologies involving quartz pressure and temperature gauges, oriented perforating, and well conditioning practices can enable operators to deploy multiple real-time downhole pressure and temperature gauges on casing across long horizontal sections of a wellbore. This, in turn, can provide valuable production data with which to understand cluster production performance, cross-well communication, fracture azimuth, well spacing, and stage-length production implications. Cemented gauges enable operators to understand pressure dynamics in the overburden, cap rock, or reservoir sections. The permanently installed, casing-deployed gauges connect to the surface through cable or through deployment of wireless inductive coupling technology.