Molecular diffusion plays an important role in oil and gas migration and transport in tight shale formations. However, there are insufficient reference data in the literature to specify the diffusion coefficients within a porous media. This study aims at calculating diffusion coefficients of shale gas, shale condensate, and shale oil at reservoir conditions with CO 2 injection for EOR/EGR. The large nano-confinement effects including large gas-oil capillary pressure and critical property shifts on diffusion coefficient are examined. An effective diffusion coefficient that describes the diffusion behavior in a tight porous solid is estimated by using tortuosity-porosity relations as well as the measured shale tortuosity from 3D imaging techniques. The results indicated that nano - confinement could affect the diffusion behavior through altering the phase properties, such as phase compositions and densities. Compared to bulk phase diffusivity, the effective diffusion coefficient in a porous shale rock is reduce by 10 2 to 10 4 times as porosity decreases from 0.1 to 0.03.

Pre-set or off-depth composite plugs can cause significant non-productive time for a well operator. In the past, fracturing operations using a composite frac or bridge plug that has been pre-set or set off depth required a coiled tubing unit or workover rig to drill the plug out. Then, the well operator could resume the fracturing job or access the wellbore below the plug. However, as this paper demonstrates, composite plug milling via wireline using a tractor and a tractor-based milling tool is a faster, safer, and more cost-effective solution. In a shale well located in the northern panhandle of West Virginia, a composite frac plug was set off- depth. Prior to mobilizing the tractor-based solution to location, the operator attempted pumping approximately 60,000 pounds of sand to sand-cut the off-depth frac plug out of the well. The sand cutting, though, did not work because perforations above the frac plug took the sand. Other tubing-based solutions required more mobilization time and complex logistics for rigging down and/or moving equipment on location. Therefore, the operator chose a wireline-based method for ease of operation, reduced HSE risk, and cost savings. The tractor took 50 minutes to drive down 1718 ft in the lateral to the plug. The milling tool milled the top slips on the frac plug in approximately nine hours, and the tractor then pushed the plug 222 ft downhole on top of the previous frac plug. The total time rigged up on the well was 14 hours, and the total time on location was 18 hours. Although this wireline-based plug-milling method takes several hours to mill a plug, the rig-up and execution is simpler than conventional methods, and associated HSE risks on the wellsite are greatly reduced. The ability to effectively release plugs via wireline provides well operators with another option to complete their objectives, especially when tubing-based methods often take many days or weeks to mobilize at substantial cost to operators.

In modern logging practices, the Poisson ratio and the Young Modulus as measures of rock Brittleness may be estimated from the dipole sonic and bulk density logs. This is important when subterranean formations are considered for fracturing or when unintended fracturing can be a concern under high injection gradients in fluid injection processes. For most oilfields, the bulk of well log suites run in the past has been limited to basic lithology logs and occasionally some porosity logs. In particular, the sheer sonic velocity log, an important component for estimation of geomechanical properties, has not been a standard measurement on common log suits. In this paper, we present the result of a study where shear travel time is correlated with measurements from caliper and shale content. The training set we used consisted of well logs for wells that included the shear travel time. We experimented with various approaches and developed a process for in cooperating DNN (deep neural network) to correlate the shear log data to other measurements.

The artificial lift system (AL) is the most efficient production technique in optimizing production from unconventional horizontal oil and gas wells. Nonetheless, due to declining reservoir pressure during the production life of a well, artificial lifting of oil and gas remains a critical issue. Notwithstanding the attempt by several studies in the past few decades to understand and develop cutting-edge technologies to optimize the application of artificial lift in tight formations, there remains differing assessments of the best approach, AL type, optimum time and conditions to install artificial lift during the life of a well. This report presents a comprehensive review of artificial lift systems application with specific focus on tight oil and gas formations across the world. The review focuses on thirty-three (33) successful and unsuccessful field-tests in unconventional horizontal wells over the past few decades. The purpose is to apprise the industry and academic researchers on the various AL optimization approaches that have been used and suggest AL optimization areas where new technologies can be developed.

Decline curve analysis is widely used in industry to perform production forecasting and to estimate reserves volumes. A useful technique in verifying the validity of a decline model is to estimate the Arps decline parameters, the loss ratio and the b-factor, with respect to time. This is used to check the model fit and to determine the flow regimes under which the reservoir produces. Existing methods to estimate the b-factor are heavily impacted by noise in production data. In this work, we introduce a new method to estimate the Arps decline parameters. We treat the loss ratio and the b-factor over time as parameters to be estimated in a Bayesian framework. We include prior information on the parameters in the model. This serves to regularize the solution and prevent noise in the data from being amplified. We then fit the parameters to the model using Markov chain Monte Carlo methods to obtain probability distributions of the parameters. These distributions characterize the uncertainty in the parameters being estimated. We then compare our method with existing methods using simulated and field data. We show that our method produces smooth loss ratio and b-factor estimates over time. Estimates using the three-point derivative method are not matched with data, and results in biased estimates of the Arps parameters. This can lead to misleading fits in decline curve analysis and unreliable estimates of reserves. We show that our technique helps in identification of end of linear flow and start of boundary dominated flow. We use our method on simulated data, with and without noise. Finally, we demonstrate the validity of our method on field cases. Fitting a decline curve using the loss ratio and b-factor plots is a powerful technique that can highlight important features in the data and the possible points of failure of a model. Calculating these plots using the Bourdet three-point derivative induces bias and magnifies noise. Our analysis ensures that this estimation is robust and repeatable by adding prior information on the parameters to the model and by calibrating the estimates to the data.

Application of horizontal drilling and hydraulic fracturing technique has made development of shale gas reservoir successful in the United States during the past decade. Chasing its operational success, researchers have been studying to understand the fundamentals of shale gas production, which will provide valuable information to assist in optimization of shale reservoir development. Unfortunately, the mechanism of shale gas production has not been fully revealed so far, and most reservoir simulation models are adopting the mechanism of coalbed methane production to forecast shale gas development process, which might not be the real case. In this paper, instead of using numerical simulation model, artificial intelligence and data mining techniques are implemented to study the controlling factors of shale gas production and understand the impacts of reservoir, completion and stimulation parameters in a dynamic manner only according to the field data. A database of Marcellus shale reservoir is generated by integrating information such as well locations, well trajectories, reservoir characteristics, completion, hydraulic fracturing, and production parameters, etc. Neural network models are trained to learn the key performance impacting factors on shale gas production in a dynamic manner, which could assist reservoir management decisions.

Improved completion design and field development strategies have provided commodity price resilience by sustained efficiency gains across most major US Shale plays. This rapid evolution in completion practices, however, has created behind pipe opportunities. Refracturing offers a viable solution to maximize on these opportunities, however, its effectiveness is dependent on a variety of factors. The present paper explores the implementation of refracturing as a re-development strategy in legacy shale plays and evaluates it as a truly multivariable problem. The paper takes into consideration petrophysical parameters, initial completion design, chemical composition, formation quality, time from original completion, refrac completion design and production performance to quantify impact on refrac KPIs such as IP ratio, EUR ratio, decline trend impact, amongst others. The paper does this by using an ACE (alternating conditional expectation) non-linear regression model that incorporates the KPI’s as response variables and utilizes the transforms of a wide range of input variables to identify cause and effect relationships. By running this analysis across multiple legacy shale plays, including the Haynesville, and Barnett, the paper provides best-practices to maximize refracturing success. While refrac can offer a viable solution in obtaining incremental production, depending on the basin, a refrac can be a tenth of the expense of a new well and can beneficially impact the production from the existing well. In most cases, the analysis found EUR predictions improved by 30% - 200%. While correlations varied across basins and completion design, an inverse correlation was found between refrac KPIs and initial frac intensity. Although, refracturing in horizontal shale wells is a well-established practice, a significant amount of analysis on their performance is focused on one or two key variables. The present paper adds to the existing body of literature by using data analytics and machine learning to evaluate this strategy from a truly multivariable standpoint. The paper also provides best practices to evaluate and predict refrac performance to de-risk refrac as a field re-development strategy.

"Sweet spots" in the unconventional reservoirs such as organic-rich mudrocks are zones with high productivity. However, identifying such regions in unconventional reservoirs depends non-only on their petrophysical and but also on their geomechanical properties. Supervised learning methods can help in integrating numerical simulation and legacy field data in sweet-spot identification workflows and enhance their analysis in complex reservoirs. The objectives of this paper are to: (i) demonstrate the use of supervised learning in parameter selection and evaluation for fracture design and (ii) provide non-linear models for sweet-spot analysis in complex reservoirs. We used fracture simulator that combines with fracture deformation with fluid-flow in discrete fracture networks. We started by selecting different geomechanical rock properties related to its fracability. We then used quasi-random design approach to obtain wide variation in aforementioned properties and performed 200 fracture simulations using the hydraulic fracturing simulator. We used the short-term Stimulated Reservoir Volumes (SRV) obtained at the end of numerical simulations, to quantify the performance of hydraulic fracturing operations. We used supervised learning techniques like support vector machines, decision trees, and random forests to perform parameter ranking and create non-linear regression models that can correlate the SRV to formation geomechanical properties. The inputs for the analysis are: initial aperture, toughness, dilation angle, closure stress, and friction coefficient of initial fractures, stress anisotropy, shear modulus and a ratio of the reservoir rock. We analyzed the results using β-linear and multinomial regression, support vector machines, decision trees, and random forests. The linear models and non-linear models can explain up to 89.1% of output variance. The classification accuracy of support vector machines was at most 35% higher than other algorithms like random forests. Parameter rating using non-linear models showed that stress anisotropy and dilation angle demonstrated the highest effect on SRVs. Shear modulus and fracture toughness show minimal effect on the SRV but these parameters might still be useful they could be correlated to other formation parameters. The outcomes of this paper demonstrated that parameters pertaining to unpropped fracture conductivity play a significant role in determining the success of hydraulic fracturing treatments. We have also compared the performances of supervised machine learning algorithms in assessing the impact of rock properties on fracturing treatments. Such supervised machine learning algorithms can help integrate field legacy data and numerical simulation outputs to develop proxy models that improve sweet-spot analysis and production estimates in unconventional reservoirs.

Hydraulic fracturing is a typical and vital technique applied in shale gas reservoir development. Numerical simulation used to be a common tool to optimize the parameters in hydraulic fracturing design determining the stage numbers, injection pressure, proppant amount, etc. However, the current understanding of shale gas storage and transport mechanism (e.g. adsorption/desorption, diffusion) is basically adopted from the lessons learned from coal seams through past experience, which might not help an efficient numerical simulation development. In this study, how artificial intelligence assisted data driven models assist the hydraulic fracturing design in shale gas reservoir is discussed. It starts by collecting field data and generate a spatial-temporal database including reservoir characteristics, operational/production information, completion/stimulation data and other variables, Neural Network models are then developed to study the impacts of all parameters on gas production as well as perform history matching of the field history. The AI assisted model with acceptable matching of field data can be used to model different hydraulic fracturing design scenarios and provide predictions on well production.

It is not easy to obtain an optimal hole cleaning for the drilling operation because of the complicated relationship between the drilling parameters influencing hole cleaning. The two viscosity components (e.g. plastic viscosity (PV) and yield point (YP)) and the flow rate (Q) are essential parameters for effective hole cleaning. Thus, understanding the relationship between those parameters will contribute to efficient hole cleaning. The aim of this paper is to explore those relationships to provide optimal hole cleaning. Descriptive data analytics was conducted for data of more than 2000 wells drilled in Southern Iraq. The data were first cleansed and outliers were removed using visual inspection and box plots. The Pearson correlation (PC), a widely used method to measure the linear relationship between two parameters, was utilized to access the relationships between PV and Q, YP and Q, and YP/PV and Q. Moreover, a 10% sensitivity analysis was escorted to quantify and comprehend those relationships. The PCs were calculated to be 0.5, 0.076, and 0.22 for the relationships between YP, PV, and YP/PV with Q, respectively. YP had the highest direct relationship with Q, while PV had the lowest. When the YP increases, a sufficient Q has to be provided to initiate the flow and maintain the mud cycle. In addition, to prevent large solid particles from settling due to the slip velocity, sufficient annular and particle velocities have to be achieved. After initiating the flow, an increase in flow rate to overcome resistance due to PV will not be significant. Therefore, YP has more effect on Q than PV. To maximize hole cleaning, thickening ratio (YP/PV) should be increased. This requires an increase in flow rate, which can be quantified by using the sensitivity analysis provided to achieve the required Q for any increase in YP/PV.

Horizontal wells with hydraulic fractures enable economical hydrocarbon extraction from unconventional reservoirs, and the associated transient production data is a reliable source for reservoir characterization. However, the complicated convolution of rate-pressure-time history leads to a less informative analysis of true reservoir characteristics. This paper presents a novel data-driven deconvolution approach using physics-based superposition to reconstruct constant-rate-drawdown pressure response, which are further translated into diagnostic plots for efficient production analysis. Traditional deconvolution in pressure transient analysis is usually an ill-conditioned "inverse" process that requires systematic curve-fitting, and the deconvolution response is highly sensitive to noise. Our proposed approach uses superposition equations as training features to honor the transient physics, and further projects them into higher dimensional ‘reservoir’ space (kernel-space) for the purpose of rigorous regression. Additionally, by implementing Laplacian eigenmaps, our algorithm is relatively insensitive to noise owing to its locality-preserving character. After training, the constant-rate-drawdown pressure response is reconstructed and a diagnostic plot is generated to identify key reservoir characteristics such as flow regimes. We first validated our approach with two synthetic cases, a horizontal well with single and multiple transverse fractures (MTFW), and the drawdown pressure response was obtained through simulation using a highly variable flow rate history. Additionally, we added artificial white Gaussian noise to the simulation output to mimic measured signals collected in the field, and we input this data into our model for deconvolution. The model-reconstructed constant-rate-drawdown pressure response was used to determine flow regimes and reservoir properties such as permeability and stimulated reservoir volume (SRV) using traditional transient testing diagnostic tools and specialized plots. The deconvolved response for each case were in alignment with the fractured-basement reservoir model proposed by Kuchuk et al. (2012) , and the flow regimes identified during MTFW production followed the theory proposed by Song et al. (2011) . All deconvolved responses were further validated through comparison with both simulation results and analytical solutions. Through the inherent locality-preserving character, our proposed algorithm was able to handle a moderate level of noise in addition to the variation of pressure-rate signals. We then applied the methodology to a field case, and the outcomes were satisfactory. This study showed that our proposed methodology is a reliable diagnostic tool to interpret pressure-rate data using traditional pressure transient analysis for unconventional reservoirs. Rapid and accurate deconvolved pressure response greatly enhances the analysis of data with moderate noise and highly variable production histories, enabling engineers to recognize flow patterns and estimate reservoir properties. We demonstrated the versatility and applicability of our proposed approach with synthetic and field cases.

Though seemingly straightforward, the concept of "net-to-gross" (NTG) is often a source of confusion. Its proper use is still being debated in some portions of the oil and gas industry. NTG is a method to account for non-reservoir quality rock when calculating oil volumes within a reservoir. This is normally accomplished by applying cutoffs to calculated quantities, such as porosity, which then get excluded from the volumetric calculation. To the extent there have been recent discussions of this, the focus has been primarily on how to determine appropriate cutoffs. There has been very little mention of the implications of using NTG in flow equations within a reservoir simulator. The paper discusses the derivation and implied assumptions for the simulator NTG formulation and possible errors and proposes modifications to account for inconsistencies. Resolving the NTG flow equations can be viewed as an upscaling problem, subject to implied assumptions about reservoir continuity. Many fine-scale reservoir simulations were run to test this and to calibrate the NTG equations. The underlying attributes were sampled from a bimodal distribution, which represent pay and non-pay. The results show the effects of NTG ratio, values of fine-scale attributes and spatial correlation on steady state, single phase effective permeability and immiscible flow displacements. They demonstrate errors in effective horizontal and vertical permeability when using NTG within a simulator. These errors cause potentially significant differences in production responses between underlying detailed fine-scale models and coarser models. The results demonstrate a possible need for corrections to the simulator net-to-gross formulations due to underlying implied assumptions and inconsistencies. Some possible modifications are also presented. Both standard and machine learning techniques were used to analyze the results.

In the past ten years, hydraulic fracturing technology and strategies have made major improvements in the operational efficiency and economic performance of shale well completions. Much of this advancement was derived in the past three years as a response to the global downturn in oil and gas commodity pricing. Mature shale plays across the United States have a surplus inventory of horizontal wells employing highly inefficient completions styles. Amid the low oil pricing environment, operators in the Bakken and Eagle Ford were capable of revitalizing these prior generation wells with great success through re-fracturing programs. In many cases, production of these re-fractured wells rivaled the production of newly drilled and completed shale wells both in terms of initial production post re-fracture as well as extended interval cumulative production. These re-fracturing programs allowed producers to achieve tremendous gains in production while minimizing drilling activity. Although re-fracturing began as a highly economical method to improve production during a time of depressed oil pricing, it is still being used today to improve the production of additional wells recognized as top-tier candidates. By developing a specific set of criteria to select wells for re-fracturing, these programs can be successfully employed in the Appalachian Basin to improve the economics of gas wells, mitigating the effects of highly discounted natural gas pricing. After the explanation of well candidacy, an economic sensitivity analysis was implemented to illustrate the impacts a strong re-fracturing program could make for operators in the Northeast through a comparison of public data showing production and total reserves for both in and out-of-basin re-fracturing programs. Additionally, while this paper focuses on re-fracturing as it relates to shale formations it also includes information as to how re-fracturing relates to conventional formations. After looking at the incremental economics of re-fracturing programs implemented in shale plays across the United States and in-basin data, the impacts of gas well re-completion can be fully quantified and understood through the application of probabilistic modeling. Additionally, this modeling further delineates re-completion candidacy by identifying which wells pose higher risks in economic metrics. Very little information has been published regarding the impacts a re-fracturing program could have in the Appalachian Basin. As the field matures, the topic of re-completions will become increasingly important, and this analysis will allow operators to have a greater understanding of the impacts of refracturing shale gas wells in the Northeast.

Drilling problems such as stick slip vibration/hole cleaning, pipe failures, loss of circulation, BHA whirl, stuck pipe incidents, excessive torque and drag, low ROP, bit wear, formation damage and borehole instability, and the drilling of highly tortuous wells have only been tackled using physics-based models. Despite the mammoth generation of real-time metadata, there is a tremendous gap between statistical based models and empirical, mathematical, and physical-based models. Data mining techniques have made prominent contributions across a broad spectrum of industries. Its value is widely appreciated in a variety of applications, but its potential has not been fully tapped in the oil and gas industry. This paper presents a review compiling several years of Data Analytics applications in the drilling operations. This review discusses the benefits, deficiencies of the present practices, challenges, and novel applications under development to overcome industry deficiencies. This study encompasses a comprehensive compilation of data mining algorithms and industry applications from a predictive analytics standpoint using supervised and unsupervised advanced analytics algorithms to identify hidden patterns and help mitigate drilling challenges. Traditional data preparation and analysis methods are not sufficiently capable of rapid information extraction and clear visualization of big complicated data sets. Due to the petroleum industry's unfulfilled demand, Machine Learning (ML)-assisted industry workflow in the fields of drilling optimization and real time parameter analysis and mitigation is presented. This paper summarizes data analytics case studies, workflows, and lessons learnt that would allow field personnel, engineers, and management to quickly interpret trends, detect failure patterns in operations, diagnose problems, and execute remedial actions to monitor and safeguard operations. The presence of such a comprehensive workflow can minimize tool failure, save millions in replacement costs and maintenance, NPV, lost production, minimize industry bias, and drive intelligent business decisions. This study will identify areas of improvement and opportunities to mitigate malpractices. Data exploitation via the proposed platform is based on well-established ML and data mining algorithms in computer sciences and statistical literature. This approach enables safe operations and handling of extremely large data bases, hence, facilitating tough decision-making processes.

The Marcellus Shale has more than a decade of development history. However, there are many questions that still remain unanswered. What is the best inter-well spacing? What are the optimum stage length, proppant loading, and cluster spacing? What are the ideal combinations of these completion parameters? And how can we maximize the rate return on our investment? This study proposes innovative tools that allow researchers to answer these questions. We build these set of tools by utilizing the pattern recognition abilities of machine learning algorithms and public data from the Southwestern Pennsylvania region of the Marcellus Shale. By means of artificial intelligence and data mining techniques, we studied a database that includes public data from more than 2,000 wells producing from the aforementioned study area. The database contained completion, drilling, and production history information from various operators active in Allegheny, Greene, Fayette, Washington, and Westmoreland counties located in the Southwestern Pennsylvania. Extensive preprocessing and data cleansing steps were involved to prepare the database. Various machine learning techniques (Linear Regression (LR), Support Vector Machines (SVMs), Artificial Neural Networks (ANNs), and Gaussian Processes (GP)) were applied to understand the non-linear patterns in the data. The objective was to develop predictive models that were trained and validated based on the current database. The predictive models were validated using information originating from numerous wells in the area. Once validated, the model could be used in reservoir management decision-making workflows to answer questions such as what are the best drilling scenarios, the optimum hydraulic fracturing design, the initial production rate, and the estimated ultimate recovery (EUR). The workflow is purely based on field data and free of any cognitive human bias. As soon as more data is available, the model could be updated. The core data in this workflow is sourced from public domains, and therefore, intensive preprocessing efforts were necessary.

Large volume slick-water stimulations have become the de facto standard for completion strategy in the Upper Devonian, Marcellus, and Utica/Point Pleasant. Current completion optimization work has focused on optimizing stage spacing, sand loading, and injection rate which have shown increases in well productivity. One commonly overlooked variable in the design equation is stimulation fluid chemistry and rock/fluid interaction. Friction reducers, the primary additive of a slickwater system, have become a commodity with many service companies providing similar systems. Premium slickwater systems in the Marcellus are generally characterized by the ability to tolerate high percentages of produced water. We have developed an alternative approach to the design of stimulation fluid chemistry. This approach consists of creating a comprehensive laboratory workflow justification for multiple fluid combinations with consideration for specific thermal maturity windows. The laboratory workflow includes proprietary rock/ fluid interaction tests that insure formation compatibility, lever imbibition/displacement production mechanisms, insure compatibility of fluid components inclusive of available water sources, and insure optimization of the fluid based on stimulation intensity ( Budney 2017 ) objectives. After extensive testing, a new stimulation fluid chemistry has been developed that offers several advantages verified by laboratory testing. The new stimulation fluid chemistry consists of a multifunctional additive with the following characteristics: salt tolerant, viscosifying, formation stabilizing, wettability enhancing friction reducer technology paired with a compatible scale inhibitor and biocide. This new stimulation fluid chemistry was field tested against an incumbent fluid chemistry provided by the stimulation service company. Well production data from the first multiple well experiment demonstrated the new stimulation fluid chemistry resulted in significantly improved well performance. A second multi-well experiment in a different area was conducted and proved the well performance improvement associated with the new stimulation fluid chemistry was repeatable. Economic analyses on wells from both field experiments demonstrate an excellent return on investment with the new stimulation fluid chemistry. This study highlights the importance of justifying stimulation fluid chemistry utilizing a laboratory workflow. The laboratory workflow incorporates rock/fluid interaction testing to maximize the imbibition/displacement production mechanism. The laboratory workflow must also prove that the stimulation fluid chemistry satisfies the stimulation intensity objectives of high rate, high sand concentration, and reduced fluid volumes while enabling reliable field execution.

Well spacing optimization is one of the most important considerations in unconventional field development. The Appalachian basin development has been increasingly prevalent in the last decade due to substantial production performance from Marcellus, Upper Devonian, and Utica/Point Pleasant Shale plays. The majority of operators have been in a manufacturing mode, where a standard well spacing and completions design have been applied to a field with less emphasis on optimizing the net asset value of the field. NAV can be improved through modeling and various optimization workflows such as numerical simulation, machine learning, and linear programming. The essence of field development and optimization is to use completions design, as well as well spacing, to optimize the net present value of the field based on current commodity pricing, capital expenditure, operating cost, cycle time, and net revenue interest. A substantial change in any of these essential factors must be studied to make sure the appropriate changes are accounted for in the field development and optimization. Determining the optimum well spacing that coincides with the completion design can often be challenging and time consuming due to complexity of some governing factors such as geology, engineering, and economic analysis. For instance, if optimum well spacing and completions design were developed in a geologically noisy and complex reservoir, the outcome may no longer be valid in a discreet and quiet area. In addition, if well spacing and completions design were developed for a high commodity pricing environment, performing the same workflow and evaluation at a lower commodity pricing would yield an increase in well spacing. The dynamic nature of the oil and gas industry, specifically these essential factors, could cause complications with selecting the optimum well spacing and completion design to maximize the shareholder's value. Therefore, a fast-paced and dynamic workflow has been developed, that can be applied in different shale reservoirs, to maximize the present value of these assets. This paper will walk through the step-by-step process that can be applied to any unconventional shale play to optimize the field considering geology, engineering, and economic analysis. The objective of this paper is to go through a fast-paced optimization workflow, starting with a fracture model, coupled with a production model using numerical simulation to obtain a calibrated model, and finally performing detailed economic and sensitivity analysis to obtain the optimum well spacing and completions design, which would yield the highest net present value of the field.

In the Appalachian Basin, the primary focus has shifted from exploratory wells to full pad development. Therefore, generational affects--the relationship between parent and child wells--are becoming a primary concern to operators that are fully developing their lease positions. This study examined bottomhole gauge data from a parent Marcellus Shale well that was recorded during the hydraulic fracture stimulation of three children wells in the Marcellus Shale and two children wells in the Burket Shale. The characteristics of the children wells created a robust data set due to variation in inter-well spacing, producing formation, completion design, and stimulation timing. Fracture modeling was performed in advance of the completion operations in order to mitigate possible parent-child communication. The parent well produced natural gas and condensate for nine months prior to being shut-in for the completion of the children wells. Rate transient analysis was performed on the parent well to further understand the depletion of the producing zone. Detailed bottomhole pressure and temperature data were measured in the parent well during the stimulation. Once operations were completed, the bottomhole gauge data was examined to identify frac hits to the parent well. The general magnitude and timing of the frac hits were examined in relation to the rock matrix and completion design parameters, and completion sequence. It was concluded that over 75 percent of Marcellus frac stages communicated with the parent well, with the most frac hits being attributed to the nearest child well. Logistic regression tests were performed on individual parameters to determine key influencers on the likelihood of a frac hit. Detailed bottomhole gauge data, as presented in this paper, is limited in the current literature due to the expense of data acquisition. The unique characteristics of the wells involved in this field experiment provide for robust statistical analysis that is not typically available publicly in the Appalachian Basin.

Current decline models fail to capture all of the behavior in shale gas production histories. That is, upon fitting one of these models, one often sees significant and sustained deviation of the flow rate data points from the decline trend. One way to measure this "lost signal" is to look at the autocorrelation in the residuals about the fitted decline model. Indeed, with many shale gas wells we see significant amounts of autocorrelation, especially when comparing the flow rate at one time to the next (lag one). Theoretically, this serially autocorrelated error can impact decline curve analysis in two ways: 1) inefficient estimation of decline curve parameters, and 2) lost signal in the data. Borrowing from time series statistics, there are two conventional ways of dealing with these potential problems: 1) estimate the decline curve parameters with generalized least squares or generalized nonlinear least squares, and 2) fitting an ARMA model to the residuals and adding it to the fitted decline curve. This paper investigates the practical implications of these two procedures by exercising them over decline curves fit to 8,527 Marcellus shale gas wells (all wells from that play with viable data for the analysis). The study explores the effect that generalized regression methods and ARMA-modeled residuals have on six different decline curves, and performance is measured in terms of sum of squared residuals (a metric for goodness-of-fit, calculated on the training data (first 24 months of each record)) and mean absolute percent error (a standard metric for forecasting accuracy, calculated on the testing data (all production rates after 24 months)). We find that inclusion of the ARMA-modeled residuals largely improves the goodness-of-fit for any decline curve, and improves the forecasting accuracy for the Hyperbolic decline curve and Duong's model. The use of generalized least squares or generalized nonlinear least squares has little benefit in fitting the decline curves, except for the Logistic Growth model, where it improves both fit and forecasting accuracy.

A new three-dimensional drillstring model has been developed that determines the static and dynamic behavior of bottom hole assemblies (BHAs) in realistic wellbores. The analysis approach has been validated with field data, and shows a strong agreement between observed and calculated BHA behavior. Several case studies are presented that show the practical use and benefit of the advanced model for, among other applications, unconventional horizontal drilling. A framework is also provided to show how the model can be incorporated into automated engineering processes for operations support. Validation tests were conducted using high-frequency down-hole data measured within a motor- assisted rotary-steerable BHA. The gathered data was used to verify the calculated mechanical loads, predicted lateral natural frequencies of the BHA, estimated directional performance of the down-hole assembly, as well as torsional resonance resulting from High-Frequency Torsional Oscillations (HFTO). Using the validated model, various analyses have been conducted for operators around the globe, in a multitude of different drilling environments, to aid in identifying drilling dysfunctions and optimizing BHA performance. Several case studies are presented that highlight the benefit of the modeling techniques in US unconventional shale plays as well as in the Canadian heavy-oil sands, with noticeable improvements in drilling efficiencies, tool design, and reduced non-productive time (NPT). Results from the field tests show a strong correlation between measured and calculated bending moment values, as well as lateral natural frequencies of the BHA with an average of 3% error across all data sets. The primary source of error is thought to be borehole spiraling, which is quantified through analysis of the down-hole bending moment data. In addition, the model is shown to provide close estimates to actual directional performance of both steerable mud motor and Rotary-Steerable BHAs. However, the directional calculation-measurement comparison does reveal a need to incorporate an ROP-dependency within the directional prediction algorithms. Nevertheless, even with these sources of discrepancy, the modeling approach provides a sensible prediction of the BHA's mechanical and dynamic behavior and, as shown through case studies, can be used as a planning tool for BHA design, an investigative tool for root-cause analysis, or potentially as a real-time optimization tool for avoiding harmful operating conditions.