Reservoir and production engineers rely heavily on well production rates to optimize well activities such as ensuring optimum reservoir monitoring. Individual gas well rates are not readily available, rather, they can be estimated thru multi-phase flow meter (MPFM) and well test analysis. These methods are associated with certain limitations such as high cost, high uncertainty, and technically elaborate calculations. Consequently, empirical and numerical calculations are employed with well test data to calculate daily rates. These practices lead to inaccurate gas rate estimations. A model with an ability to provide accurate estimates of gas rates for a gas reservoir can serve as a handy tool for the subsurface engineers in addressing well and reservoir optimization strategies. This work presents artificial intelligence models to estimate gas rates in a gas field containing ten wells. The aim is to develop a correlation that is simple and easy to incorporate yet providing robust answers on a global scale. Multiple machine learning tools are employed. These include; Artificial Neural Network (ANN), Functional Network (FN), and Adaptive Neuro Fuzzy Inference System (ANFIS). Production data from a dry gas field X was used for the model development. Data cleaning and data reduction steps were carried out to ensure the input parameters for the proposed model are physically relevant and accurate. Missing these steps would result in the development of an erroneous correlation, i.e., garbage -in garbage-out (GIGO). This led to finalization of certain basic well-head parameters which are available at any typical well and had direct impact on the output production rate. The target parameter for model training is the gas rate. A rigorous comparison between the investigated artificial intelligence models was conducted by calculating average absolute percentage error (AAPE) and coefficient of determination. The comparative analysis shows that the intelligent model is able to predict the gas rate in condensate wells with accuracy in excess of 90%. Examples of such large accuracy has not been reported previously. ANN performs a step ahead as compared to the various intelligent algorithms used in this study. This paper sheds light on the potential of the Industrial Revolution 4.0 for the Pakistani Oil and Gas Sector. Data-driven artificial intelligent models are capable of validating the well test and multiphase flow meter results. In addition, it can prove to be a vital tool in an engineer's tool-kit to reduce uncertainties in gas rate measurements.

In Potwar Basin, Tight Naturally Fractured carbonate reservoirs usually have matrix porosity of the 2-3% and permeability less than 1 mD. It is very challenging to evaluate Gas initial in-place (GIIP) and reserves accurately in these type of reservoirs. The objective of this study is to evaluate Gas in-place and to characterize the reservoir energy mechanism using rate transient analysis (RTA). A comparison has also been made for Gas in-place from conventional material balance analysis and from rate transient analysis. In this study, rate transient analysis has been performed in a well, located in Potwar with thirteen years of production history. The well is completed in Chorgali and Sakesar formations. Different scenarios of initial reservoir pressure, geo-mechanical effects during initial production of the well and aquifer volume were sensitized to evaluate the Gas initial in-place (GIIP) and reserves. Different type curves such as Agarwal Gardner, Blasingame and flowing material balance and analytical aquifer modelling with / without geo-mechanical effects were applied and matched with the production history. Production logging data was also incorporated in the study. During history matching (gas-rate and bottom-hole flowing pressure), different sensitivities were run to quantify the uncertain parameters and level of uncertainty in the simulator. While history matching and evaluating GIIP/reserves, it was observed that 8,060 psi reservoir pressure measured in the well was not stabilized and the reservoir was dominated initially by geo-mechanical effects. The best match was achieved with 10,935 psi initial reservoir pressure which was measured in the first well of the field. Without considering this pressure and geo-mechanical effects, the best match could not be obtained. It was also confirmed by different type curves that reservoir was geo-mechanically pressured during the early life of the well and also, it has a weak aquifer support during the late life of the well. The GIIP evaluated from these type curves are in the range of 75-90 BSCF whereas conventional material balance showed 60-70 BSCF indicative volume associated with the well. This study exhibited a significant difference in GIIP/reserves evaluated from conventional material balance and rate transient analysis, indicating remaining hydrocarbon potential in the region i.e. approximately 10-13 BSCF. If the static reservoir pressure is taking too long to stabilize, the conventional material balance approach always depicts lower hydrocarbon volume associated with the well due to the tight and heterogeneous nature of the reservoirs. Therefore, conventional material balance cannot be applied in these reservoirs and unconventional approach such as rate transient analysis should be applied to evaluate GIIP / reserves and reservoir energy mechanism.

A major challenge in carbonate reservoirs is the highly-fractured nature of the rock. The flow rate may be high or low depending on the targeted fracture clusters. In addition, it is possible that flow rates vary from one region of the reservoir to another. Smart wells furnished with smart completion strategy presents great prospects to produce such reservoirs intelligently, thereby, helping to deal with heterogeneities rather smartly. It is established that early water break-through occurs when multi-lateral wells are completed with constant choke settings, and therefore one way to mitigate this problem is using smart completions that manage the unexpected production through fractures, thereby increasing ultimate recovery. The early water breakthrough is obvious because if a lateral section intersects a clusters of fracture zone, there is a possibility that these fractures may connect with the water zone that may trigger the breakthrough. This can be managed by preferentially regulating production from manifold laterals. The evident communication among the various laterals of the mother bore raises difficulty in optimizing the production from the variable productivity intervals. In theory, the optimization scheme of smart completion involves different constraints, nevertheless, the settings of the smart inflow control valve (ICV) is the single most important parameter that may prove to be the differentiating factor between a high producing well to a poorly producing one. This study engrosses its effort on the reservoir engineering characteristics of finding the optimum choke setting that would lead to maximum recovery. Computational Intelligence through Particle Swarm Optimization (PSO) is utilized as the integral algorithm to determine the optimal ICV configuration for a fishbone well in a naturally fractured carbonate reservoir. A commercial black oil simulator was used to determine the objective function; whose role here is to evaluate the fitness of a configuration of the choke; this was carried out under a workflow programmed in the MATLAB programming language that coupled the optimization algorithm with the numerical simulator. A single fishbone well, having 15 laterals was studied in order tot see the effect of the fracture network on the water breakthrough and consequent impact on recovery. Three different scenarios are developed to see the impact of optimization; a base case employing only multilateral well technology without the smart well completion, a smart well completion scheme with no optimization and finally the optimized smart well completion. The results very sequentially clarify the need for not only optimization but also highlights the role of intelligent completions for wells in the reservoir being studied. It is evident that without using smart wells, the water breakthrough is relatively earlier and produces less hydrocarbons, but as the use of smart wells is incorporated, the results start improving and for complete optimization scheme of the ICVs, it is observed that the recovery has increased by almost 80% from 21% to 38%. Moreover, the time to water breakthrough and eventually the cumulative water cut has also been managed quiet significantly.

The paper presents a case study to improve recovery in a heterogeneous depletion drive oil reservoir by pressure maintenance through waterflooding, which was designed by analyzing seismic attribute and well tests data and incorporating it in a reservoir simulation model. X Field is an undersaturated oil field which has been accessed by two wells. It is an extremely heterogeneous reservoir with X-1 being a prolific producer while X-2 having no production due to significant facies variation in the reservoir over a short distance (~2km). The field displays depletion drive behavior with rapid pressure decline over low recovery. A reservoir simulation study was undertaken to improve recovery and arrest pressure decline. Attribute analysis and well tests data helped address heterogeneity of the reservoir by predicting the facies distribution throughout the reservoir. These learning's were incorporated into a reservoir simulation model and a line drive water injection pattern was determined as the optimal method to improve recovery in X Field. A new well, X-WIW, was drilled to start injection and sidewall cores were acquired from it. Multiple lab tests were performed on these cores to confirm injected water compatibility with the formation while the well's injection rates and surface parameters were closely monitored to help minimize reservoir damage and achieve continuous injection. Using seismic attribute analysis, a good facies zone was identified for drilling X-WIW for line drive water injection pattern. Well results showed good quality sand which laid further credence to the facies distribution model. MDT showed depleted pressure in X-WIW, therby confirming connectivity between X-1 and X-WIW. Sidewall cores were obtained and multiple tests performed, including Capillary Suction Test (CST), XRD Analysis, Sludge Test and Core Flow Test, which showed no presence of swelling clays and good formation compatibility with injection water. Prior to injection, X-1 was shut in to preserve reservoir energy and current bottomhole reservoir pressure was measured. Upon receiving these lab results, injection was initiated successfully, with X-WIW injecting 2500 bwpd while keeping X-1 shut-in. Periodic measurements of bottomhole pressure were taken at X-1 to observe injection response. The pressure at X-1 was measured to be steadily increasing indicating successful voidage replacement. The most significant new finding was the accurate prediction of the pattern of variation of petrophysical properties through seismic attribute analysis and well tests data which enabled X-WIW to be a successful injector and helped in improving the recovery of X Field. Prior to this water injection project, X Field was predicted to have a recovery factor of 21% which has now been improved to 35%. This significant increase in recovery was made possible by the successful implementation of the water flooding project in a heterogeneous and stratigraphically complex field.

Creating reservoir model that is accurate requires extensive data and enormous computing resources. As these resources are often not available, this study proposes a workflow (& corresponding case study) to generate box reservoir models that require minimum data yet yield reasonable results. Field Lima has two wells Lima-1 & Lima-2 (down-dip well) that produces gas from Sand-B which is a moderate water drive reservoir. After producing gas naturally, Lima-2 watered-out while Lima-1 still produced gas. To curtail the increase of Water Gas Ratio (WGR) in Lima-1, a coproduction technique was evaluated. The intent was to produce water from Lima-2 at high rates to reduce WGR increase in Lima-1. This required reservoir simulation to predict if producing water from Lima-2 will limit aquifer encroachment at Lima-1 and justify the additional CAPEX required to install water handling facility at Lima-2. As Lima is a marginal field, available dataset only included; initial reservoir pressure, production history and standard open-hole logs. No permeability, aquifer or pressure history data was available. Therefore, a workflow was formulated to create a reservoir model that can predict production rates of Lima-1 using available data within engineering accuracy. Firstly, the pressure history was created using recorded flowing wellhead pressures and wellbore hydraulics model. Further, a tank model was created for the reservoir using material balance software and cumulative productions of both wells were used as unique data to calibrate the model. This was used to calculate the aquifer properties. Fractional flow models & Corey correlations were used to estimate relative permeability data. All this derived data was then used as input in the reservoir simulator, where a localized block grid was created using depth-structure maps of Sand-B. The model was history matched using production data of Lima-1 & Lima-2 and aquifer movement was calibrated using water breakthrough in Lima-2. Finally, the model was run to predict production rates of Lima-1 with & without keeping Lima-2 on production to evaluate if co-production technique was effective. The model predicted that Lima-1 will be able to deliver its production targets even without keeping Lima-2 on production suggesting that extra CAPEX required for co-production was not justified. Forecasted gas rates for Lima-1 matched the actual gas rates with around 10% error. The accuracy of forecasted results endorses the use of box reservoir models to undertake economic decisions, in scenarios where lack of time & data doesn’t allow creation of detailed dynamic reservoir models.

Reservoir simulation models are constructed with a large amount of geophysical, geological, petro physical and dynamic data. Ideally, it should represent the historical dynamic behavior of the reservoir but due to insufficient characterization there is a need to adjust reservoir parameters to calibrate reservoir models. This is achieved by the history matching process in reservoir simulation. Conventionally it is done by iteratively changing the most sensitive global/local parameters with high degree of uncertainty. Most history matching problems are complex, both concerning the number of parameters being adjusted and the level of accuracy required which makes it a time consuming process. Assisted history matching (AHM) has been a subject of interest in the recent past with many developments in its technology. A loop of changes directs the key reservoir parameters to minimize the mismatch defined inside the assisted history matching application. This aids in decision making by obtaining a number of calibrated reservoir models where each model can be used for performance prediction to obtain representative field development scenario(s) with minimized uncertainties/risks in minimum time and a cost effective manner. The assisted history matching workflow used for this case study consists of the following components: Sensitivity analysis Uncertainty analysis Tests and improve the reservoir model (Simulation vs Observed matching) Improve efficiency, speed and quality of the results Example of history matching of single well field will be compared on performance improvement using manual versus assisted history match. The reservoir model had 90k grid cells cells. Complex near critical nature of fluids were present in the subject field. The well was matched over 6 years of history. Uncertain reservoir parameters (water cut, static pressure) were selected to fine tune the well behavior. The performance difference, between manual history matching and assisted history matching, was measured through total time required for the HM, number of runs and quality of pressure match obtained at the field level.

Forecasting the performance of individual wells is of great importance to oil companies for continuous field production optimization and cost per barrel reduction. Well Performance is measured by evaluating the Inflow Performance & Outflow Performance Relationships (IPR & OPR). For that the need to use the correct IPR for subjected reservoir is very essential. Since many decades, Vogel IPR equation has been used comprehensively for evaluating the productivity of vertical wells in saturated gas drive reservoirs. Vogel's model successfully evaluates IPR for single porosity solution gas drive in homogenous reservoirs. However, the applicability of Vogel's IPR for naturally fractured dual porosity-dual permeability reservoirs is questionable; hence this challenge is addressed by the industry only numerically. Due to composite fluid flow behavior between fracture and matrix system in a dual porosity-dual permeability reservoir, there is a pressing need for a new, practical and simple IPR model that takes into account the consideration of fracture parameters. This study investigates the effects of reservoir complex fluid flow behavior on IPR curves for vertical wells drilled in naturally fractured oil saturated reservoirs resulting in the development of an IPR curve for such conditions. A base case simulation model is developed with typical reservoir, fluid and rock properties using black oil model. Using a set of points relating oil production rates to flowing bottom-hole pressures, the dimensionless IPR curves are generated. Fluid and reservoir properties were varied between two porous and permeable media such as Inter-porosity flow co-efficient, storativity ratio, oil gravity, reservoir thickness and bubble point pressure. Inter-porosity flow coefficient and fracture storativity ratio were found to have a significant impact on dimensionless IPR curves. Finally, an attempt is made to converge the results into one simple model using non-linear regression technique in order to get a new empirical IPR correlation for vertical well producing from naturally fractured oil reservoirs. A small and acceptable average absolute error of less than 2% was found with the new proposed empirical IPR model, while comparing the existing published correlations on the same data gives more than 15% error. The developed IPR correlation is accurate enough and can serve as a handy tool for the production engineers to forecast the productivity of wells drilled in a naturally fractured dual porosity-dual permeability saturated oil reservoirs.

As the demand of natural gas is increasing along with decline in conventional gas resources, the exploitation of unconventional resources has become essential to meet the increasing demand. Shale being unconventional hydrocarbon resource; is very complex in terms of fluid flow and storage mechanisms and require advanced techniques to be modeled correctly. As high investments are involved to develop the resource, therefore representative model is required to decide whether to exploit a shale resource or not, and optimize the development strategy in order to maximize the recovery and profitability. Mostly shale resources are modeled using analytical methods in which best fit model on one well is applied on all other wells of the field to simulate their behavior. However this may not be the case in actual due to heterogeneities in the reservoir. One way to simulate the performance of shale is to use a numerical simulation model in which one can predict the effect of various scenarios on production such as horizontal length, number of stages of frac, etc. In addition it allows to optimize various parameters for the development of field which may not be done through analytical models. In this paper a numerical simulation model is developed for a shale resource. This model is used as a tool for planning and optimizing the development strategy of the resource. The model contains horizontal wells with multi stage fracturing and is validated using well production data of a shale resource. Vertical Lift Performance (VLP) curves are generated from a well modeling software and are incorporated in the simulation model. Horizontal development wells are incorporated to maintain a specific plateau rate. Sensitivity of various parameters such as number of wells, timing of the wells and plateau rate is carried out to study various cases. The results of these cases including plateau rate and period and gas recoveries are presented and the optimum case is selected based on the profitability and maximum recovery. The adopted approach can be used efficiently to evaluate a shale gas prospect to establish its development and appraisal strategy.

It is a well-known fact that the deliverability of condensate reservoirs is a function of condensate formation around the wellbore. Thus, accurate estimation of condensate recovery necessitates properly modeled Equation-of-State (EOS) and subsurface flow phenomena. EOS is a function of numerous parameters of the Reservoir and Fluid contained; however it should be kept in mind that it is almost impossible to exactly match all these parameters, while all the parameters don’t obligate equal significance as well. This paper describes the most important parameters in EOS which ultimately control the overall condensate recovery. It involves unique simulation methodology to determine the condensate flow in reservoir using velocity-dependent-relative-permeability (VRP) curves (instead of conventional-relative permeability methods) to determine the effect of velocity stripping near the well bore; and the Pseudo-pressure approach. The modified form of EOS used in the study, along with the VRP curves features accurate condensate flow in the reservoir and its optimization. Moreover, using the pseudo pressure approach, this paper also signifies the reservoir modeling and forecasting methods with minimum amount of time for simulation. A complete and comprehensive strategy has been presented in this paper, to infer the factors having max influence on recovery. Using diverse sensitivities, it is evident that four parameters have a high influence; which includes compositional-variation with depth, Liquid drop-out, Condensate viscosity and Compressibility, while the rest yield minimal impact. The VPR models also improve history matching and give better forecast; while using pseudo-pressure approach reduces significant time with negligible impact on the results.

As the global HC exploitation paradigm shifts to the unconventional (low-quality) reservoirs; the deep uncertainty in their production processes involved makes the operators, following the Specific Discipline Approach, vulnerable to adopt the trial-and- error methods which can detriment the whole development plan and assimilate irreparable loss upon the foul decisions. Since each engineering discipline may have a piece of puzzle to find the best solution for achieving optimized production rates and economics, we can only hope to achieve the better results if we use a total system (reservoir to sales approach). Pakistan is facing the severe energy crisis, due to which the effective and sensible exploitation of the tight gas reserves has become inevitable. Integrated Asset Modelling (IAM) constructs a thorough way out by increasing communication and literally gets all disciplines "on the same page" by using a common model. The paper is based on designing a full field development plan of a tight gas reservoir (TGRs). It involves developing an integrated reservoir model and evaluating its performance for various reservoir and production scenarios, along with the economic analysis, to come up with an optimum gas field development plan. The methodology is carried out with two approaches (i) Integrated Tank Model (ii) Numerical 3D Model. The paper presents the usefulness of IAM and its systematic approach in production maintenance and optimization, crossfunctional collaboration and in effective field management of tight gas reservoirs. It will present detailed discussion on the economics, process cycle, modelling, optimization, game-changers and exploitation of TGRs along with case studies and intends to give valuable insight to the implementation of this integrated technique to the prevalent energy crisis of the country.

Adhi field is located in Punjab province of Pakistan in Potwar region and was discovered in 1978. Adhi structure consists of main northeast-southwest (NE-SW) trending popup structure with a relief of ~600m and extensions separated by faults in the NE and southern flanks. So far 10 wells have successfully penetrated Tobra & Khewra reservoirs with most of the wells drilled along the fold axis. The PVT analyses based on the initial wells show the reservoir to contain rich retrograde condensate fluid with an initial condensate to gas ratio of ~150 bbls/MMscfd. Production from the Tobra and Khewra reservoirs started in 1990 with the installation of 25 MMscfd LPG processing plant. The plant capacity was doubled in 2006. The field presents complex geology because of the presence of extensive faulting and high relief structure (600-700m). The field’s production performance is complicated by the variation in the PVT behavior of the produced fluids with production and the presence of possible oil rim. The field is estimated to contain ~1120 Bcf of wet gas and more than 160 MMstb of Liquid Hydrocarbons (Condensate and Oil). The reservoir pressure dropped below the dewpoint in 1992-93 and the reservoirs have been producing below dewpoint for the last 18 years. The most challenging part in planning the further enhancement of production and maximization of recoveries from the reservoirs is the prediction of the composition of production stream in the future. Both the reservoirs are experiencing significant change in fluid properties for the last 5-6 years. The produced condensate used to be transparent, which has gradually turned black with API gravity decreasing in almost half of the wells from the initial 55° to 35°. A three dimensional compositional reservoir simulation study was initiated to have more reliable prediction of the complex compositional behavior of field production. The study has completed the fluid characterization with matching of lab results for saturation pressure, Constant Composition Expansion (CCE) & Constant Volume Depletion (CVD). The tuned Equation of State (EOS) has been used in compositional reservoir simulation for history matching and predictions. However, for matching the high density fluid breakthrough, rigorous tuning of equation of state (EOS) was required. The sensitivity of critical condensate saturations S cc was also being attempted to achieve history match at well level. Reasonable HM of reservoir pressure has been achieved; with change in oil API and CGR & Tubing Head Pressure (THP) with assigned uncertainty range of 10-20% due to the complex nature of reservoir behavior. This paper discusses in depth, the challenges of compositional reservoir simulation, describing formulation of EOS & its subsequent matching, establishing the reservoir fluid gradient & gravity segregation with their impact on HM. The future development strategies are also discussed with the stand point of reservoir management to increase recoveries from such challenging reservoirs.

Because stochastic seismic inversion generates a large number of elastic impedance realizations, only a few of these realizations must be selected for more rigorous geological interpretation and reservoir modelling. Static sand body volume (SBV) and SBV geometrically connected to the wells are generally used as the ranking measures. However, this approach does not take into account the dynamic flow field and the impact of reservoir heterogeneity and petrophysical properties on the fluid flow. Hence, this approach under or overestimates the P10, P50 & P90 ranking most of the time and invalidates the field development studies in turn. The main goal of this study is to prove the concept of using dynamic measures to rank the seismic inversion realizations and develop a ranking work flow. Streamline simulations, because of its generic option of reduced flow physics provides a faster way to run hundred of millions cell realizations, enabling a quick ranking based on dynamic measures. Typical seismic inversion models consists of several million cells (6 million in this case), and flow simulation over models of this size is computationally extensive. To reach reasonable runtimes, the size of the model is reduced; either by extracting a sector model (2 million cells sector in this case), or by decimating/up-scaling the full field model (1.7 million cells in this case). Tracer simulations over both of the reduced size models are compared with the full field tracer simulation model. The upscaled model is shown to be a closer approximation to the full field model. Finally, the tracer upscaled model is compared with a physically more realistic waterflood upscaled simulation model and is shown as a valid approximation. The sensitivity of various dynamic parameters to connectivity variations is analyzed to determine which parameter is the best suited ranking measure. Due to significant variability in spatial sand bodies distribution and connectivity between the sand bodies across the realizations, cumulative oil recovery (COR) has proved to be a much better ranking measure in comparison to the recovery factor (RF). The static and dynamic ranking measures are compared. The difference between SBV or connected sand body volume (CSBV) and COR rankings highlights the importance of ranking based on dynamic measures. Additionally, the factors which can generally influence the ranking based on dynamic measure (COR) are determined and it is concluded that the changes in ranking will be dependent on the CSBV and petrophysical properties of the connected sand bodies across the realizations. Finally, ranking based on the best ranking measure as well on a combination of COR, STOIIP and RF is done and individual realizations (P10, P50 and P90) are selected in order to cover the range of uncertainty in spatial distribution and volumes of sand bodies. This uncertainty range can be translated into economic uncertainty for the determined optimum number of wells and distances. The approach defined in this paper to rank and select the P10, P50 & P90 realizations based on dynamic measures is generic and can be applied to any form of geo-statistical realizations. The streamline simulations proved to be an effective screening tool.

Subject paper discusses the success story of Gas Reservoir Simulation Model having more than 85 wells spread over the reservoir area within Gas-Water Contact (GWC) around 200,000 acres, built in Eclipse™ Simulator. The gas field has production history of over 40 years. The pipeline network of different sizes around 250 Km managing the whole system and successfully deliver the committed volume to different customers. Production Forecast cases need to be run which cater the network impact by implementation of surface operating conditions with subsurface Reservoir Simulation Modeling.