The harsh conditions presented in Brazilian presalt, summed up with the complexity of its reservoir, generate a series of challenges to improve reservoir recovery. Routinely, we have used intelligent completion systems to address the major part of the challenges; however, with the new production rates new problems have arrived and the usual ones have turned more aggressive, generating risks even to the intelligent completion systems. Inorganic scale is a critical challenge in presalt reservoirs production. Future plans for presalt production include more robust flow conditions and the use of an all-electrical completion system. Higher flow rates are likely to increase the risk of scale deposition and an optimum design is required. To address the new challenges arising from the new perspective of exploration in the presalt fields, we developed the presented workflow to mitigate the scale deposition on completion valves. The method enables the optimization of choke geometry to reduce scale deposition on inflow control valves. The proposed workflow generates a criticalness parameter for geometry classification according to a scenario of mechanical failure (due to sleeve incapacity to move) or deviation of production design point. A computational fluid dynamics (CFD) simulation was developed and benchmarked by experimental data, thus CFD results for different scenarios and various choke geometries were used to build a risk analysis matrix, which allows the definition of the optimal choke design to mitigate scale on ICVs. The extracted criticalness parameter may be used as an evaluator to estimate the time to valve stuck due to scale deposition in a commercial 1D transient flow simulator, optimizing then valve cycling time.

Objectives/Scope A 60-year-old well exhibited a leak at the surface and sustained annuli pressure (SAP) in all three annuli - A, B and C. The age of the well coupled with the symptoms suggested a complex multi-barrier failure scenario that needed to be diagnosed quickly and accurately so that the well could be secured safely. The paper shows how the comprehensive set of downhole diagnostics, including metal and cement barriers integrity assessment, aided in planning and execution of well intervention to secure the indicated failures. The well intervention team selected an approach to investigate the barriers status prior the rig came to the location. Surface diagnostics included evaluation of pressure trends, completion design, leak fluid composition and drilling records analysis which guided the selection of downhole survey. The input parameters for downhole through barrier diagnostics were: – SAP observed in all annuli; – Questionable cementing of downhole tubulars; – Poor material selection to withstand H 2 S and CO2 concentrations; Downhole through barrier diagnostics targeted the evaluation of leak paths and failures in well barriers and included: – Casings metal wall thickness profiling by Electro-Magnetic(EM) pulse logging tools; – Seal evaluation of metal and integrity of cement barriers evaluation by passive acoustic logging tools;-Tracking of the fluid migration within the well system by high precision temperature. EM pulse survey was conducted after the tubing retrieval. This paper explores the concept, planning, execution and achieved results of the downhole diagnostics performed and how the results were implemented by Operator.

There are advantages to using high performance lightweight cement when encountering low bottomhole pressures. The most notable are maintaining wellbore stability during cement placement and the isolation of potential flow zones to achieve the wellbore construction objectives. Several complex wells sought these advantages for similar situations. A review of the deployment process for using high performance lightweight cement conventionally, including the quality assurance measures, initially deemed it as not a viable option. As the complex wells needed a technical solution, an unconventional deployment method for high performance lightweight cement enabled its use while simplifying and improving quality assurance; allowing achievement of the isolation requirements.

Carbonate reservoirs are challenging for chemical EOR, particularly in selecting fine-tuned chemical formulations which combine high performance, stable behavior, and trouble-free operations. The design of suitable formulations requires substantial laboratory work and a solid methodology. In this paper, a systematic all-inclusive laboratory workflow to design a surfactant-polymer (SP) formulation for a carbonate reservoir is presented. In this work, a complete process for development and evaluation of an SP formulation for high-salinity high-temperature conditions is proposed and adopted. For which, a high throughput robotic platform is used for efficient and robust formulation design. The process is illustrated on an actual case with harsh reservoir conditions (i.e. a high temperature of 100℃ and high connate salinity of 213,000 mg/L). The SP design methodology consisted of five steps: surfactant design, polymer selection, surfactant/polymer verification, topside assessment, and oil-displacement evaluation. The surfactant formulation design consisted of four substeps: solubility scans, phase-behavior scans (salinity scans), IFT measurements, and static adsorption tests. The sourced polymers were screened based on three key performance indicators: viscosity, filter ratio, and thermal stability. The selected surfactant formulations and polymers were then assessed as sloppy slugs in terms of compatibility and injectivity. Then, the unique topside assessment was conducted where it consisted of two components focusing on: separation kinetics and separated water quality. Finally, an oil displacement study was performed using a preserved composite plug, in which the SP formulation developed through the outlined process was used. The results demonstrate the potential of a mixture of Olefin Sulfonate (OS) and Alkyl Glyceryl Ether Sulfonate (AGES). The results also illustrate couple of polymers with stabilities suitable for high temperature conditions: an associative polymer, and an AM/AMPS copolymer. In addition, injectivity corefloods supported the SP slug transportability across the porous media. Corefloods also demonstrated the SP slug capacity to recover around 62% ROIC (remaining oil in core). Finally, SP in produced brines improved the separation kinetics but lead to a slight deterioration in separated water quality. A key novelty of the adopted workflow is the integration of topside assessment. In addition, the experimental steps were clearly delineated including the preparation of representative oils. Beside a clear layout of the methodology, the work demonstrates that a surfactant-polymer formulation can successfully be designed for high temperature carbonate reservoirs and provide encouraging guidelines with respect to SP impact on topside facilities.

Transverse relaxation (T2) times measured by multi-frequency, multi-gradient nuclear magnetic resonance (NMR) logging tools are affected by diffusion-induced enhanced relaxation which reduces the sensitivity to pore size in slow-relaxing formations such as macroporous carbonates and complicates the integration with zero-gradient core NMR data. We propose a solution for eliminating the diffusion-related uncertainties using intrinsic T2 distributions, obtained by a new inversion-forward modeling-inversion (IFMI) method, for carbonate pore typing applications. The NMR logs presented in this paper are based on data measured at five frequencies where the static magnetic field gradient varies from 26 to 55 G/cm. The high-quality echo signals are processed using a three-step IFMI differential signal analysis approach which nullifies diffusion effects due to the tool gradient and the potentially present internal gradient caused by paramagnetic minerals in the formation. The resulting diffusion-free intrinsic T2 distribution accentuates fine pore size variations and allows better discernment of micro-, meso-, and macropore systems of complex carbonate reservoirs. Multi-frequency NMR data, acquired in multiple wells, were processed and analyzed in several ways. First, apparent T2 distributions were obtained separately for individual frequencies. Discrepancies between the results of different frequencies clearly indicated that in macro- and mesoporous carbonates the diffusion effect is significant even with TE=0.3ms. This leads a peak broadening observed in the apparent T2 spectrum from conventional NMR processing, where echo trains from different frequencies are averaged in time-domain prior to the inversion. With the IFMI processing, individual-frequency echo trains are first pre-processed using a 2D NMR inversion whose results are used to forward model a diffusion-free echo train without prior assumptions on reservoir fluid diffusivity D. A second inversion, applied on the diffusion-free echo train, yields the intrinsic T2 distribution. The intrinsic T2 distribution has a noticeably higher spectral resolution in carbonate formations where diffusion effect is significant. The intrinsic T2 logs are expected to be more consistent with other gradient-free NMR measurements such as core NMR or LWD NMR data sets.

Customers in Ecuador inject the byproduct formation water from production wells into injector wells. A limited injection rate bottlenecks production, which is economically undesirable. Two major contributors limit injection capacity: reservoir injectivity and flowline pressure losses. In the latter case, paraffins, asphaltenes, and scale, collectively referred to as "schmoo," progressively build in the flowline and reduce the internal diameter, limiting flow rate capacity. One cost-effective method to remediate flowlines with significant deposits is coiled tubing (CT) cleanouts. This unconventional method, which calls for optimized planning, execution, and performance evaluation, has been implemented in five flowlines. An economic analysis shows that remediating flowlines using CT cleanout yields significant savings as compared with replacement. After a candidate is identified, job planning takes into consideration flowline length and deviation (to identify maximum reach of CT), schmoo analysis (to design an optimal bottomhole assembly and fluid treatment), and execution logistics (to ensure a viable, reliable, and safe operation). After the cleanout, the flowline is put back into service, and the effectiveness of the treatment is estimated based on system flow rates and pressure losses. The equivalent internal diameter (ID) for the flowlines was improved by over 49% in each of the remediated flowlines, achieving an effectiveness of over 89% of nominal ID and increasing flow rates without a detrimental effect on system pressure. The cleanouts re-established nominal capacity in over 50k ft of flowline that no longer needed replacement. Lessons learned include the ability to complete the cleanout with water alone. The chemical analysis in planning stages showed the absence of carbonates, which enabled a mechanical cleanout with a high-pressure nozzle. Nonetheless, a chemical treatment was designed as a contingency. Another learning was that whereas tubing force models helped predict the reach of the CT, other factors created limitations. For example, the weld bead on the flowline limited the reach of the CT and required re-evaluating where to create cuts along the flowline. Finally, deploying the CT in a flowline required configuring the injector head horizontally, which required a customized base for safe rig up and operation of the injector head and pressure-control equipment. CT successfully cleaned out five flowlines with IDs ranging from 6-in. to 8-in. and re-established 89% to 98% of their nominal ID. As a result, the operator saved upwards of USD 14 million in flowline replacement costs, increased asset utilization, and decreased deferred injection. Historically, there is limited documented experience with flowline cleanouts using CT. The paper documents a repeatable methodology for candidate selection, planning, execution, and performance evaluation. It also provides basic building blocks to meet treatment design, rig-up, and execution requirements that are unique to this application.

Although visual data analytics using image processing is one of the most growing research areas today and is largely applied in many fields, it is not fully utilized in the petroleum industry. This study is inspired by medical image segmentation in detecting tumor cells. This paper uses a supervised Machine Learning technique through video analytics to identify bit dullness that can be used in the drilling industry in place of the subjective screening approach. The evaluation of bit performance can be affected by subjective evaluation of the degree of dullness. The present approach of using video analytics is able to grade bit dullness to avoid user subjectivity. The approach involves the use of datasets in good quantity and quality by separating them into training datasets, testing datasets, and validation datasets. Due to the large datasets, Google Collaboratory was used as it provides access to its Graphic Processing Unit (GPU) online for the processing of the bit datasets. The processing time and resource consumption are minimized using Google GPU. Using the Google GPU resources, the procedure is automated without any installation. After the bit is pulled out and cleaned, a video is taken around and up and down in 360°. Further, it is compared against the green bit. By this approach, multiple video datasets are not required. The algorithm was validated with new sets of bit videos and the results were satisfactory. The identification of the dullness or otherwise of each screened bit is done with the aid of a bounding box with a stamp of a level of confidence (range 0.5–1) and the algorithm assigns for its decision on the identified or screened object. This method is also able to screen multiple bits stored in a single place. In an event where several drill bits are to be screened, manual grading will be a huge task and will require a lot of resources. This model and algorithm will take a few minutes to screen and provide grading for several bits while videos are passed through the algorithm. It has also been found that the grading with the video was much better than the single image as the contextual information extracted are much higher at the level of the entire video, per segment, per shot, and per frame. Also, methodology is made robust so that the video model test starts successfully without error. The time penalty for the processing is fast and it took less time for a single video screening. The work developed here is probably the first to handle the dull bit grading using video analytics. With more of these datasets available, the future automation of the IADC bit characterization will soon evolve into an automated process.

Optimized geomodeling and history matching of production data is presented by utilizing an integrated rock and fluid workflow. Facies identification is performed by use of image logs and other geological information. In addition, image logs are used to help define structural geodynamic processes that occurred in the reservoir. Methods of reservoir fluid geodynamics are used to assess the extent of fluid compositional equilibrium, especially the asphaltenes, and thereby the extent of connectivity in these facies. Geochemical determinations are shown to be consistent with measurements of compositional thermodynamic equilibrium. The ability to develop the geo-scenario of the reservoir, the coherent evolution of rock and contained fluids in the reservoir over geologic time, improves the robustness of the geomodel. In particular, the sequence of oil charge, compositional equilibrium, fault block throw, and primary biogenic gas charge are established in this middle Pliocene reservoir with implications for production, field extension,and local basin exploration. History matching of production data prove the accuracy of the geomodel; nevertheless, refinements to the geomodel and improved history matching were obtained by expanded deterministic property estimation from wireline log and other data. Theearly connection of fluid data, both thermodynamic and geochemical, with relevant facies andtheir properties determination enables a more facile method to incorporate this data into the geomodel. Logging data from future wells in the field can be imported into the geomodel allowingdeterministic optimization of this model long after production has commenced. While each reservoir is unique with its own idiosyncrasies, the workflow presented here is generally applicable to all reservoirs and always improves reservoir understanding.

Conventional methods of Heavy Oil production have either delivered low recovery factors or have involved costly thermal projects. With low oil prices the new norm, Operators need to maximize their recovery factors whilst controlling CAPEX required to exploit their assets. This paper presents a new approach to lifting heavy/viscous oil with a Pump design that will allow Operators to increase overall field production with reduced energy consumption. A trial was conducted in Colombia in a harsh field (high viscosity, low water cut, high temperature and high gas production) that had challenged conventional artificial lift systems as Electrical submersible pumps and progressive cavity pumps. Several strategies (light oil injection, high capacity equipment, solvents injection at surface) had been implemented to overcome fluid viscosities 6,000 cP @ 150 °F at dead oil conditions and a mean of 250 cp inside the pump, with excellent short and medium-term results but system hydraulic efficiency and energy consumption were still below expectations. A new pump has been specially designed for the fluid conditions previously described, with the main objective of reduce the friction losses inside the pump stage increasing the overall efficiency and reducing energy consumption. Initial trial results have fulfilled the study's objectives, overcoming the issues previously experienced with ESP operations in this field. Designed to handle heavy, high viscosity oil, this study has demonstrated that there is significant economic benefit through savings and improved efficiencies and, in addition, ESP capital cost savings. Previously, oversized units were routinely used to deliver the required flow and pump power but by optimizing the sizing of the ESP system, it is possible to select pumps with fewer stages and lower horsepower motors to suit the new requirements. Data from the field tests have delivered encouraging results. The pump has operated smoothly since installation and, in comparison to standard pump, has reduced energy consumption (savings of 10-15%) and shown a 15-20% increase in pump efficiency. The Operator has reasonable optimistic expectations for future installations in this field in all applicable wells.

Unconventional reservoir development is a multidisciplinary challenge due to complicated physical system, including but not limited to complicated flow mechanism, multiple porosity system, heterogeneous subsurface rock and minerals, well interference, and fluid-rock interaction. With enough well data, physics-based models can be supplemented with data driven methods to describe a reservoir system and accurately predict well performance. This study uses a data driven approach to tackle the field development problem in the Eagle Ford Shale. A large amount of data spanning major oil and gas disciplines was collected and interrogated from around 300 wells in the area of interest. The data driven workflow consists of: Descriptive model to regress on existing wells with the selected well features and provide insight on feature importance, Predictive model to forecast well performance, and Subject matter expert driven prescriptive model to optimize future well design for well economics improvement. To evaluate initial well economics, 365 consecutive days of production oil per CAPEX dollar spent (bbl/$) was setup as the objective function. After a careful model selection, Random Forest (RF) shows the best accuracy with the given dataset, and Differential Evolution (DE) was used for optimization. Using recursive feature elimination (RFE), the final master dataset was reduced to 50 parameters to feed into the machine learning model. After hyperparameter tuning, reasonable regression accuracy was achieved by the Random Forest algorithm, where correlation coefficient (R2) for the training and test dataset was 0.83, and mean absolute error percentage (MAEP) was less than 20%. The model also reveals that the well performance is highly dependent on a good combination of variables spanning geology, drilling, completions, production and reservoir. Completion year has one of the highest feature importance, indicating the improvement of operation and design efficiency and the fluctuation of service cost. Moreover, lateral rate of penetration (ROP) was always amongst the top two important parameters most likely because it impacts the drilling cost significantly. With subject matter experts’ (SME) input, optimization using the regression model was performed in an iterative manner with the chosen parameters and using reasonable upper and lower bounds. Compared to the best existing wells in the vicinity, the optimized well design shows a potential improvement on bbl/$ by approximately 38%. This paper introduces an integrated data driven solution to optimize unconventional development strategy. Comparing to conventional analytical and numerical methods, machine learning model is able to handle large multidimensional dataset and provide actionable recommendations with a much faster turnaround. In the course of field development, the model accuracy can be dynamically improved by including more data collected from new wells.

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.

As the most potential Carbon Capture, Utilization, and Storage (CCUS) technology, CO2-enhanced oil recovery (CO 2 -EOR) can both improve oil recovery and relieve the pressure of reducing CO 2 emission. However, CO 2 -EOR projects have not been substantially deployed in China due to the significant investment and high uncertainties of technology, market, and policy. Therefore, identifying potential bottlenecks, and developing effective investment strategies are of great necessity at present. In this work, a real option approach combined with reservoir simulation technologies is proposed, which can investigate the optimal deployment timing and the investment value of the CO 2 -EOR projects. Meanwhile, a sensitivity analysis is conducted to examine the effects of different uncertainties. The results show that real option approach is suitable for the evaluation of CO 2 -EOR projects because it can fully take the flexibility of investment time into account. And it is found that under the current investment environment, it is difficult for China to deploy CO 2 -EOR projects on a large scale before 2030. High oil prices, low CO 2 purchase prices, and transportation of CO 2 by pipeline can bring forward the investment time and increase the investment value. Besides, government subsidies and technological progress are also favorable for the deployment of the project. Compared with technological progress, the effect of subsidies is more obvious, while it should be noted that huge subsidies will bring a financial burden to the government. In a word to launch CO 2 -EOR projects earlier and make it play a more important role in China's carbon emission reduction, a compound strategy should be made based on consideration of all these influencing factors.

This study aims at evaluating the mechanical integrity and the changes in the pore system of illitic sandstone formations being treated with HF acid generated in-situ and thermochemical fluids ( TCF ). Firstly, the acid-generating fluids were injected separately inside the core within a typical core flooding experiment. After that the core permeability, NMR porosity and rock static and dynamic parameters such as rock strength ( UCS ), dynamic Young's modulus, Poisson's ratio, shear modulus and bulk compressibility were evaluated before and after the treatment. Sand production prediction model was used to examine the potential of sand production problems for the cores treated with the in-situ generated acid ( ISGA ) and the TCF . Treating Scioto cores with the ISGA only led to permeability enhancement with final to initial permeability ratio K f i n a l K i n i t i a l of 1.2. In addition, treating the same cores with acid generating fluids and thermochemicals resulted in higher permeability enhancement with K f i n a l K i n i t i a l of 485. This higher enhancement in the formation permeability was attributed to the creation of some microfractures as was proved using the NMR scans. There was a reduction of the cores' static and dynamic parameters after being treated with the ISGA. This reduction was even higher after treating the core with thermochemical fluids. However, the sand production prediction model stated that the reduction in rock mechanical parameters would not lead to sand production problems.

Improving ESP performance has become one of the main challenges in gassy wells. Different methods and new technologies have been developed but most of them have high economic value. A new system has been created to prevent large amounts of gas flowing directly into the pump intake through the breaking of the gas slugs and the resolubilization (dispersion) of the free gas into the production liquid. The system contains an isolation section which allows to break gas slugs and pressurize that section forcing the gas to go in solution to be produced afterwards. The concept of the new technology is to manage the Rs (Gas in solution) with pressure, additionally the technology includes a vortex effect creator which will improve the gas dispersion iton the liquid associated and will separate the sand production. The ESP’s Downhole Regulator was designed based on each well conditions to maximize its efficiency. This paper summarizes the design and application of the new Vortex Regulator in more than 50 gassy wells completed in the Wolfcamp A, Wolfcamp B, Lower Spraberry, and Jo Mill C in the Permian basin. The performance evaluation was carried out by a constant monitoring of the sensor parameters through a certain period. During the evaluation period the ESPs in most of the wells were operating under stable conditions with minor shutdowns, no significant increases in the motor current, motor temperature, and vibration were observed.

Unconfined compressive strength (UCS) equally represented as geomechanical strength remains a critical mechanical property in the successful implementation of key technologies for shale gas reservoirs’ development and production. Attention has been less concentrated on prediction models’ development for shale geomechanical strength evaluation. Majority of the existing shale geomechanical strength correlations are dependent on single log input parameter, which is insufficient to account for the complex and non-linear behaviour of UCS across the entire reservoir interval of interest. The high relevance of UCS has therefore triggered the need for the application of an integrated system of principal component – multivariate regression analysis in driving UCS predictive models’ development for shale gas reservoirs. Generated acoustic datasets of notable shale gas reservoirs (Marcellus, Montney, Longmaxi and Roseneath) in respective countries (United States of America (USA), Canada, China and Australia) were used. Statistical test analysis was conducted in validation for wider applications of the developed UCS prediction models. Models development were driven by 21,708 datapoints of acoustic parameters, models’ accuracy ratings were above 99%, R-squared values had high degrees of closeness to unity, mean absolute percentage error (MAPE) values were at less than 10% and coefficient of variation (COV) at less than (1.0). UCS prediction models were all dependent on multiple direct log measured acoustic parameters in distinction to existing UCS empirical correlations; thus, a pure reflection of significant boost to the accuracy and reliability of UCS measurements for shale gas reservoirs. The developed prediction models will promote geomechanical strength accountability and lead to creation of a robust base in minimization of wellbore instability problems, optimization of wellbore trajectory and containment of hydraulic fractures. This will significantly contribute in putting gas resources of shale reservoirs with enormous potentials, at the forefront of quantitatively meeting natural gas requirements in global energy demand.

An innovative technology called Data Physics* , which combines the predictive capacity of traditional numerical simulators with the speed and flexibility of machine learning, was applied to optimize a mature waterflood in the Neuquen basin in Argentina. A reservoir model was built by combining machine learning techniques with the partial differential equations of fluid flow as applied in conventional reservoirs simulatiors. The approach is probabilistic in nature, thereby supporting the quantification of uncertainty. Given the complex conditions of operation and control in secondary recovery projects, the dearth of data, which was inherently noisy, it is not feasible to define a unique deterministic solution. The proposed approach provides physically possible and statistically viable distributions (ranges and frequencies) of the variables affecting reservoir behavior. Due to its predictive capabilities, the reservoir model, created at a layer level using raw uninterpreted production and injection data, is capable of reproducing and predicting field production history. This enables continuous closed loop optimization and provides a program of injection water redistribution. The result is optimized production, and reserves development with reduced injection costs. As part of the assessment of different prescriptions, the operator wanted to evaluate the optimum way to cycle two sets of injectors to reduce water handling and injection costs. A robust connectivity model was built from the reservoir model which was then used to evaluate and compare the different alternatives to cycle injectors during two years versus an optimum injection redistribution. The results include production forecasts for the scenarios evaluated which, when executed, would allow the customer to reduce water cost while maintaining production levels unaltered.

This case study presents a success story of a development well in the Gulf of Mexicodrilled with oil-based mud, with latest high-definition borehole image and comprehensive formation evaluation-while-drilling suite. Historical data from multiple offset wells suggested that the standardresolution petrophysical logs were insufficient to accurately evaluate this low-resistivity, low-contrastreservoir. The presence of salt intrusions compromises the surface seismic interpretation and poseadditional geological challenges, adding uncertainty to the oil water contact—which, if incorrectlyidentified, would cause early water production and compromise the success of the well. Deploying a unique, high-resolution dual-physics imaging-while-drilling system and the advanced petrophysical LWD logging suite for the first time in this field was critical toachieving the optimal reservoir characterization and making subsequent operational decisions. Real-timecapture-spectroscopy and sigma measurements were essential to evaluate the mineralogical compositionand saturation estimation. Water saturation computed from sigma gives an estimate of free- and bound fluid volume, independent of the resistivity measurement, which showed little contrast betweenhydrocarbon- and water-filled formations. The real-time transmission of apparent resistivity images enabled the identification and interpretation of the low-resistivity low-contrast reservoir, which appeared to be comprised of cross-bedded, sand-silt laminated facies. High-definition borehole images detected features such as fine lamination and fractures, which were not possible to be identified with the standard-resolution logging suite previously deployed in this field. This information provided an insight into the reservoir architecture and possible extent of the depositional facies vertically and laterally. The ultrasonic high- and low-frequency borehole images further helped refine the understanding of reservoir architecture in relevant time to aid in completion design. Additionally, the apparent resistivity derived from the high-definition resistivity provided a proxy to ultra-shallow resistivity that was able to identify the movable fluid in the reservoir when compared to standard resistivity measurements in singular drilling pass and confirmed the oil-water contact which were a critical information for well completion design This case study demonstrates the integration of petrophysics and geological interpretation of comprehensive LWD logging which has been fundamental to resolve the reservoir characterization uncertainties and optimizing the completion strategy in a challenging low resistivity reservoir in one drilling run with real-time transmission of high-quality data.

Full field production profile is needed to evaluate enhanced oil recovery (EOR) option and to progress the EOR project. In general, the methodology of full-field profile estimation highly depends on the objective case: field development maturity level, technology readiness level of the EOR option to be planned, etc. Therefore, this paper is facilitating how we should select an adequate methodology to estimate full-field EOR profiles through comparison of four case studies. Case-1 was picked up as an example of type curve method based on sector models for water alternating gas (WAG) application using CO 2 or hydrocarbon miscible gas. Case-2 represented a full-field simulation approach to incorporate facility capacities into account adequately because of produced gas re-injection (i.e. gas EOR). Case-3 demonstrated to use the identical flow model, which was history-matched with long production records, for estimating a new CO 2 foam EOR technology which was unavailable as option in the identical commercial simulator. The last Case-4 dealt an emerging microbial EOR/EGR process without any commercial simulator and no pilot data yet. In Case-1, two type curves were generated to represent typical geologies in the objective field: homogeneous and heterogeneous areas. After sensitivity studies using the simple sector models to optimize each parameter, type curves were obtained from the detailed sector models. The type curves, assigned in corresponding areas, were summed to estimate full-field profile. In Case-2, even full-field simulation is debatable in general due to huge workload and computation, it still has important role to evaluate gas EOR with limited gas processing capacity because to ignore the facility limits might mislead to optimistic conclusion. In Case-3 in the mature field, the history-matched model was already established by a commercial simulator. Unfortunately, the simulator does not have an exact option to evaluate our emerging CO 2 foam technology while available for the conventional foam EOR using surfactant as foaming agent. Thus, we managed to handle our EOR technology by matching laboratory experimental outputs with pseudo-calculated gas mobility reduction ratio. In Case-4, another emerging microbial EOR technique was estimated analytically even only laboratory experimental data was available. Any commercial simulator is not available, either. The unique approach took essence of experimental outputs into a well type curve, and then full-field profile was estimated. Each workflow has pros/cons, and an adequate one should be selected. However, in usual, a unique workflow is just applied to estimate full-field profile in the evaluation of objective EOR option. Furthermore, there has been little discussion of workflow selections from the aspect of development stage, EOR technological emerging level, and evaluation tool availability. This paper can provide ideas to consider guidelines for generating full-field profiles.

This paper presents the evolution of completion sand control designs for the Peng Lai field in Bohai Bay, offshore China. A range of sand control designs from open hole gravel packs to open hole standalone screens to cased hole high rate water packs and cased hole frac-packs have been tried over time. Evaluation of these various designs indicates that single-trip multi-zone cased hole frac-pack designs provide the most effective method for developing the thick, low net-to-gross, unconsolidated sandstone reservoirs in this waterflood field. The paper describes the completion design and evaluation process used in the field and shows how field challenges led to innovative frac-pack downhole equipment designs. This paper highlights completion practices and results from a range of well types used to develop the large Peng Lai field, offshore China. Development challenges and productivity and reliability results for alternative completion designs are discussed on the basis of field performance. The resulting preferred design, consisting of cased hole frac-packs using large-bore and small-bore single-trip multi-zone equipment are then discussed in further detail with emphasis on development of an innovative, fit-for-purpose, small-bore design. The installation challenges, solutions and successes experienced during the design and installation process and the actual performance and benefits of the new system are also discussed in this paper. Over the course of Peng Lai field development, a range of alternative completion sand control designs have been tried and evaluated. Cased hole frac-packs provide the most effective completion method for the thick, low net-to-gross, sandstone reservoirs. To enable effective completions in sidetracked wells, a 7" single-trip multi-zone frac pack tool has been developed. This small-bore system has been used to complete 13 wells with 67 zones in the field to date, saving over 43 rig days and $8MM. This paper presents reliability and productivity comparisons for a range of alternative completion sand control designs in a single, large field environment. A new 7" single-trip, fit-for-purpose design has been developed for the field and successfully implemented in multiple well campaigns. Installation time data and cost savings are provided.

What is the next best drilling decision to make in a Field Development Plan (FDP)? This is the key question we address in this work. A FDP consists of a sequence of decisions. Each action we take affects the reservoir and conditions any future decision. The novelty of our proposed approach is the consideration of the sequential nature of the decisions through the framework of Dynamic Programming (DP) and Reinforcement Learning (RL). In this framework, each scheduled drilling decision depends on the observations acquired between drillings. This methodology allows moving the focus from a static Field Development Plan optimization to a more dynamic framework that we call Field Development Policy Optimization. In addition to the formulation of this new framework, we have applied this methodology to optimize the development of a real oil and gas field. We model the FDP optimization problem under subsurface uncertainty as a Partially Observable Markov Decision Process (POMDP) and solve it through a RL algorithm in order to find an optimal drilling policy. Our methodology works for a general reservoir with a given set of geological model representing. To speed up the learning process we utilize a trained Deep Recurrent Neural Network (RNN) to approximate the reservoir simulator flows, which are subsequently used to compute the economic performance of a drilling policy through its discounted cash flows. The RNN is trained and tested on a set of reservoir simulator runs over randomly sampled realizations of our reservoir model, well location, type and control sequences of a drilling plan. From all the possible decisions involved in a FDP, we focus here only on finding optimal adaptive well drilling schedules (locations of vertical wells and well types). The RL agent learns the best drilling schedule policy by generating simulated episodes of experience and iteratively improving the policy using a Q value function approximated by a neural network trained across episodes. The final solution consists of an adaptive Field Development Plan yielding the highest expected Net Present Value (NPV), computed within a given, available time budget. It specifies an adaptive drilling schedule of producer and injector, well locations and well controls, as a function of the information obtained at each drilling step. The methodology has been applied to an actual reservoir for infill well location decisions. In this case, our objective is finding the best well placement and well type for the next producer and injector wells, as well as the optimization of the control schedule for new and preexisting wells in the reservoir. Our results show the learning progress of our RL algorithm until finding the optimal drilling plan. The robustness of the solution is evaluated across the best-trained policies. Methodology and results have been validated using a brute force sampling approach. Both RL and brute force approaches were possible due to our fast-to-compute RNN approximation of the reservoir simulator. This work represents, to our knowledge, the first application of an end-to-end AI workflow for Field Development Policy Evaluation in real fields, based on Reinforcement Learning and Deep Learning. The proposed methodology puts together an optimal field evaluation in planning and a surveillance workflow for a reactive decision-making.